import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
from matplotlib.pyplot import show
df=pd.read_csv('Downloads/training_set.csv')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1460 entries, 0 to 1459 Data columns (total 81 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1460 non-null int64 1 MSSubClass 1460 non-null int64 2 MSZoning 1460 non-null object 3 LotFrontage 1201 non-null float64 4 LotArea 1460 non-null int64 5 Street 1460 non-null object 6 Alley 91 non-null object 7 LotShape 1460 non-null object 8 LandContour 1460 non-null object 9 Utilities 1460 non-null object 10 LotConfig 1460 non-null object 11 LandSlope 1460 non-null object 12 Neighborhood 1460 non-null object 13 Condition1 1460 non-null object 14 Condition2 1460 non-null object 15 BldgType 1460 non-null object 16 HouseStyle 1460 non-null object 17 OverallQual 1460 non-null int64 18 OverallCond 1460 non-null int64 19 YearBuilt 1460 non-null int64 20 YearRemodAdd 1460 non-null int64 21 RoofStyle 1460 non-null object 22 RoofMatl 1460 non-null object 23 Exterior1st 1460 non-null object 24 Exterior2nd 1460 non-null object 25 MasVnrType 1452 non-null object 26 MasVnrArea 1452 non-null float64 27 ExterQual 1460 non-null object 28 ExterCond 1460 non-null object 29 Foundation 1460 non-null object 30 BsmtQual 1423 non-null object 31 BsmtCond 1423 non-null object 32 BsmtExposure 1422 non-null object 33 BsmtFinType1 1423 non-null object 34 BsmtFinSF1 1460 non-null int64 35 BsmtFinType2 1422 non-null object 36 BsmtFinSF2 1460 non-null int64 37 BsmtUnfSF 1460 non-null int64 38 TotalBsmtSF 1460 non-null int64 39 Heating 1460 non-null object 40 HeatingQC 1460 non-null object 41 CentralAir 1460 non-null object 42 Electrical 1459 non-null object 43 1stFlrSF 1460 non-null int64 44 2ndFlrSF 1460 non-null int64 45 LowQualFinSF 1460 non-null int64 46 GrLivArea 1460 non-null int64 47 BsmtFullBath 1460 non-null int64 48 BsmtHalfBath 1460 non-null int64 49 FullBath 1460 non-null int64 50 HalfBath 1460 non-null int64 51 BedroomAbvGr 1460 non-null int64 52 KitchenAbvGr 1460 non-null int64 53 KitchenQual 1460 non-null object 54 TotRmsAbvGrd 1460 non-null int64 55 Functional 1460 non-null object 56 Fireplaces 1460 non-null int64 57 FireplaceQu 770 non-null object 58 GarageType 1379 non-null object 59 GarageYrBlt 1379 non-null float64 60 GarageFinish 1379 non-null object 61 GarageCars 1460 non-null int64 62 GarageArea 1460 non-null int64 63 GarageQual 1379 non-null object 64 GarageCond 1379 non-null object 65 PavedDrive 1460 non-null object 66 WoodDeckSF 1460 non-null int64 67 OpenPorchSF 1460 non-null int64 68 EnclosedPorch 1460 non-null int64 69 3SsnPorch 1460 non-null int64 70 ScreenPorch 1460 non-null int64 71 PoolArea 1460 non-null int64 72 PoolQC 7 non-null object 73 Fence 281 non-null object 74 MiscFeature 54 non-null object 75 MiscVal 1460 non-null int64 76 MoSold 1460 non-null int64 77 YrSold 1460 non-null int64 78 SaleType 1460 non-null object 79 SaleCondition 1460 non-null object 80 SalePrice 1460 non-null int64 dtypes: float64(3), int64(35), object(43) memory usage: 924.0+ KB
df['SaleCondition'].value_counts()
Normal 1198 Partial 125 Abnorml 101 Family 20 Alloca 12 AdjLand 4 Name: SaleCondition, dtype: int64
df.shape
(1460, 81)
df.isna().sum()
Id 0
MSSubClass 0
MSZoning 0
LotFrontage 259
LotArea 0
...
MoSold 0
YrSold 0
SaleType 0
SaleCondition 0
SalePrice 0
Length: 81, dtype: int64
for i in df.columns:
if (df[i].isna().sum())>0:
if df[i].dtypes=='object':
x=df[i].mode()[0]
df[i]=df[i].fillna(x)
else:
x=df[i].mean()
df[i]=df[i].fillna(x)
df.isna().sum()
Id 0
MSSubClass 0
MSZoning 0
LotFrontage 0
LotArea 0
..
MoSold 0
YrSold 0
SaleType 0
SaleCondition 0
SalePrice 0
Length: 81, dtype: int64
X=df.drop(labels=['SalePrice','Id','LowQualFinSF','MiscVal'],axis=1)
Y=df['SalePrice']
X.shape
(1460, 77)
Y.shape
(1460,)
cat=[]
con=[]
for i in X.columns:
if X[i].dtypes=='object':
cat.append(i)
else:
con.append(i)
print(cat)
print(con)
['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'] ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold']
cat=['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'Fence', 'SaleType', 'SaleCondition']
con=['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'MoSold', 'YrSold']
for i in df.columns:
if df[i].dtypes=='object':
df[i].value_counts().plot(kind='bar')
plt.show()
else:
sb.histplot(data=df,x=df[i],kde=True)
plt.show()
for i in df.columns:
if df[i].dtypes=='object':
sb.boxplot(data=df,x=df[i],y='SalePrice')
plt.show()
else:
plt.scatter(data=df,x=df[i],y='SalePrice')
plt.xlabel(i)
plt.ylabel('SalePrice')
plt.title(f'{i} vs SalePrice')
plt.show()
a=df.corr()
C:\Users\PRATHMESH\AppData\Local\Temp\ipykernel_288228\962033958.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. a=df.corr()
a
| Id | MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | ... | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Id | 1.000000 | 0.011156 | -0.009601 | -0.033226 | -0.028365 | 0.012609 | -0.012713 | -0.021998 | -0.050199 | -0.005024 | ... | -0.029643 | -0.000477 | 0.002889 | -0.046635 | 0.001330 | 0.057044 | -0.006242 | 0.021172 | 0.000712 | -0.021917 |
| MSSubClass | 0.011156 | 1.000000 | -0.357056 | -0.139781 | 0.032628 | -0.059316 | 0.027850 | 0.040581 | 0.022895 | -0.069836 | ... | -0.012579 | -0.006100 | -0.012037 | -0.043825 | -0.026030 | 0.008283 | -0.007683 | -0.013585 | -0.021407 | -0.084284 |
| LotFrontage | -0.009601 | -0.357056 | 1.000000 | 0.306795 | 0.234196 | -0.052820 | 0.117598 | 0.082746 | 0.179283 | 0.215828 | ... | 0.077106 | 0.137454 | 0.009790 | 0.062335 | 0.037684 | 0.180868 | 0.001168 | 0.010158 | 0.006768 | 0.334901 |
| LotArea | -0.033226 | -0.139781 | 0.306795 | 1.000000 | 0.105806 | -0.005636 | 0.014228 | 0.013788 | 0.103960 | 0.214103 | ... | 0.171698 | 0.084774 | -0.018340 | 0.020423 | 0.043160 | 0.077672 | 0.038068 | 0.001205 | -0.014261 | 0.263843 |
| OverallQual | -0.028365 | 0.032628 | 0.234196 | 0.105806 | 1.000000 | -0.091932 | 0.572323 | 0.550684 | 0.410238 | 0.239666 | ... | 0.238923 | 0.308819 | -0.113937 | 0.030371 | 0.064886 | 0.065166 | -0.031406 | 0.070815 | -0.027347 | 0.790982 |
| OverallCond | 0.012609 | -0.059316 | -0.052820 | -0.005636 | -0.091932 | 1.000000 | -0.375983 | 0.073741 | -0.127788 | -0.046231 | ... | -0.003334 | -0.032589 | 0.070356 | 0.025504 | 0.054811 | -0.001985 | 0.068777 | -0.003511 | 0.043950 | -0.077856 |
| YearBuilt | -0.012713 | 0.027850 | 0.117598 | 0.014228 | 0.572323 | -0.375983 | 1.000000 | 0.592855 | 0.314745 | 0.249503 | ... | 0.224880 | 0.188686 | -0.387268 | 0.031355 | -0.050364 | 0.004950 | -0.034383 | 0.012398 | -0.013618 | 0.522897 |
| YearRemodAdd | -0.021998 | 0.040581 | 0.082746 | 0.013788 | 0.550684 | 0.073741 | 0.592855 | 1.000000 | 0.179186 | 0.128451 | ... | 0.205726 | 0.226298 | -0.193919 | 0.045286 | -0.038740 | 0.005829 | -0.010286 | 0.021490 | 0.035743 | 0.507101 |
| MasVnrArea | -0.050199 | 0.022895 | 0.179283 | 0.103960 | 0.410238 | -0.127788 | 0.314745 | 0.179186 | 1.000000 | 0.263582 | ... | 0.159349 | 0.124965 | -0.109849 | 0.018795 | 0.061453 | 0.011723 | -0.029815 | -0.005940 | -0.008184 | 0.475241 |
| BsmtFinSF1 | -0.005024 | -0.069836 | 0.215828 | 0.214103 | 0.239666 | -0.046231 | 0.249503 | 0.128451 | 0.263582 | 1.000000 | ... | 0.204306 | 0.111761 | -0.102303 | 0.026451 | 0.062021 | 0.140491 | 0.003571 | -0.015727 | 0.014359 | 0.386420 |
| BsmtFinSF2 | -0.005968 | -0.065649 | 0.043340 | 0.111170 | -0.059119 | 0.040229 | -0.049107 | -0.067759 | -0.072302 | -0.050117 | ... | 0.067898 | 0.003093 | 0.036543 | -0.029993 | 0.088871 | 0.041709 | 0.004940 | -0.015211 | 0.031706 | -0.011378 |
| BsmtUnfSF | -0.007940 | -0.140759 | 0.122156 | -0.002618 | 0.308159 | -0.136841 | 0.149040 | 0.181133 | 0.114184 | -0.495251 | ... | -0.005316 | 0.129005 | -0.002538 | 0.020764 | -0.012579 | -0.035092 | -0.023837 | 0.034888 | -0.041258 | 0.214479 |
| TotalBsmtSF | -0.015415 | -0.238518 | 0.363358 | 0.260833 | 0.537808 | -0.171098 | 0.391452 | 0.291066 | 0.362452 | 0.522396 | ... | 0.232019 | 0.247264 | -0.095478 | 0.037384 | 0.084489 | 0.126053 | -0.018479 | 0.013196 | -0.014969 | 0.613581 |
| 1stFlrSF | 0.010496 | -0.251758 | 0.414266 | 0.299475 | 0.476224 | -0.144203 | 0.281986 | 0.240379 | 0.342160 | 0.445863 | ... | 0.235459 | 0.211671 | -0.065292 | 0.056104 | 0.088758 | 0.131525 | -0.021096 | 0.031372 | -0.013604 | 0.605852 |
| 2ndFlrSF | 0.005590 | 0.307886 | 0.072483 | 0.050986 | 0.295493 | 0.028942 | 0.010308 | 0.140024 | 0.174019 | -0.137079 | ... | 0.092165 | 0.208026 | 0.061989 | -0.024358 | 0.040606 | 0.081487 | 0.016197 | 0.035164 | -0.028700 | 0.319334 |
| LowQualFinSF | -0.044230 | 0.046474 | 0.036849 | 0.004779 | -0.030429 | 0.025494 | -0.183784 | -0.062419 | -0.069068 | -0.064503 | ... | -0.025444 | 0.018251 | 0.061081 | -0.004296 | 0.026799 | 0.062157 | -0.003793 | -0.022174 | -0.028921 | -0.025606 |
| GrLivArea | 0.008273 | 0.074853 | 0.368392 | 0.263116 | 0.593007 | -0.079686 | 0.199010 | 0.287389 | 0.389893 | 0.208171 | ... | 0.247433 | 0.330224 | 0.009113 | 0.020643 | 0.101510 | 0.170205 | -0.002416 | 0.050240 | -0.036526 | 0.708624 |
| BsmtFullBath | 0.002289 | 0.003491 | 0.091481 | 0.158155 | 0.111098 | -0.054942 | 0.187599 | 0.119470 | 0.085055 | 0.649212 | ... | 0.175315 | 0.067341 | -0.049911 | -0.000106 | 0.023148 | 0.067616 | -0.023047 | -0.025361 | 0.067049 | 0.227122 |
| BsmtHalfBath | -0.020155 | -0.002333 | -0.006419 | 0.048046 | -0.040150 | 0.117821 | -0.038162 | -0.012337 | 0.026669 | 0.067418 | ... | 0.040161 | -0.025324 | -0.008555 | 0.035114 | 0.032121 | 0.020025 | -0.007367 | 0.032873 | -0.046524 | -0.016844 |
| FullBath | 0.005587 | 0.131608 | 0.180424 | 0.126031 | 0.550600 | -0.194149 | 0.468271 | 0.439046 | 0.275730 | 0.058543 | ... | 0.187703 | 0.259977 | -0.115093 | 0.035353 | -0.008106 | 0.049604 | -0.014290 | 0.055872 | -0.019669 | 0.560664 |
| HalfBath | 0.006784 | 0.177354 | 0.048258 | 0.014259 | 0.273458 | -0.060769 | 0.242656 | 0.183331 | 0.200802 | 0.004262 | ... | 0.108080 | 0.199740 | -0.095317 | -0.004972 | 0.072426 | 0.022381 | 0.001290 | -0.009050 | -0.010269 | 0.284108 |
| BedroomAbvGr | 0.037719 | -0.023438 | 0.237023 | 0.119690 | 0.101676 | 0.012980 | -0.070651 | -0.040581 | 0.102417 | -0.107355 | ... | 0.046854 | 0.093810 | 0.041570 | -0.024478 | 0.044300 | 0.070703 | 0.007767 | 0.046544 | -0.036014 | 0.168213 |
| KitchenAbvGr | 0.002951 | 0.281721 | -0.005805 | -0.017784 | -0.183882 | -0.087001 | -0.174800 | -0.149598 | -0.037364 | -0.081007 | ... | -0.090130 | -0.070091 | 0.037312 | -0.024600 | -0.051613 | -0.014525 | 0.062341 | 0.026589 | 0.031687 | -0.135907 |
| TotRmsAbvGrd | 0.027239 | 0.040380 | 0.320146 | 0.190015 | 0.427452 | -0.057583 | 0.095589 | 0.191740 | 0.280027 | 0.044316 | ... | 0.165984 | 0.234192 | 0.004151 | -0.006683 | 0.059383 | 0.083757 | 0.024763 | 0.036907 | -0.034516 | 0.533723 |
| Fireplaces | -0.019772 | -0.045569 | 0.235755 | 0.271364 | 0.396765 | -0.023820 | 0.147716 | 0.112581 | 0.247906 | 0.260011 | ... | 0.200019 | 0.169405 | -0.024822 | 0.011257 | 0.184530 | 0.095074 | 0.001409 | 0.046357 | -0.024096 | 0.466929 |
| GarageYrBlt | 0.000070 | 0.080187 | 0.064324 | -0.024812 | 0.518018 | -0.306169 | 0.780555 | 0.618130 | 0.249367 | 0.150338 | ... | 0.220623 | 0.218490 | -0.285882 | 0.023534 | -0.075256 | -0.014499 | -0.031853 | 0.005173 | -0.000987 | 0.470177 |
| GarageCars | 0.016570 | -0.040110 | 0.269729 | 0.154871 | 0.600671 | -0.185758 | 0.537850 | 0.420622 | 0.363778 | 0.224054 | ... | 0.226342 | 0.213569 | -0.151434 | 0.035765 | 0.050494 | 0.020934 | -0.043080 | 0.040522 | -0.039117 | 0.640409 |
| GarageArea | 0.017634 | -0.098672 | 0.323663 | 0.180403 | 0.562022 | -0.151521 | 0.478954 | 0.371600 | 0.372567 | 0.296970 | ... | 0.224666 | 0.241435 | -0.121777 | 0.035087 | 0.051412 | 0.061047 | -0.027400 | 0.027974 | -0.027378 | 0.623431 |
| WoodDeckSF | -0.029643 | -0.012579 | 0.077106 | 0.171698 | 0.238923 | -0.003334 | 0.224880 | 0.205726 | 0.159349 | 0.204306 | ... | 1.000000 | 0.058661 | -0.125989 | -0.032771 | -0.074181 | 0.073378 | -0.009551 | 0.021011 | 0.022270 | 0.324413 |
| OpenPorchSF | -0.000477 | -0.006100 | 0.137454 | 0.084774 | 0.308819 | -0.032589 | 0.188686 | 0.226298 | 0.124965 | 0.111761 | ... | 0.058661 | 1.000000 | -0.093079 | -0.005842 | 0.074304 | 0.060762 | -0.018584 | 0.071255 | -0.057619 | 0.315856 |
| EnclosedPorch | 0.002889 | -0.012037 | 0.009790 | -0.018340 | -0.113937 | 0.070356 | -0.387268 | -0.193919 | -0.109849 | -0.102303 | ... | -0.125989 | -0.093079 | 1.000000 | -0.037305 | -0.082864 | 0.054203 | 0.018361 | -0.028887 | -0.009916 | -0.128578 |
| 3SsnPorch | -0.046635 | -0.043825 | 0.062335 | 0.020423 | 0.030371 | 0.025504 | 0.031355 | 0.045286 | 0.018795 | 0.026451 | ... | -0.032771 | -0.005842 | -0.037305 | 1.000000 | -0.031436 | -0.007992 | 0.000354 | 0.029474 | 0.018645 | 0.044584 |
| ScreenPorch | 0.001330 | -0.026030 | 0.037684 | 0.043160 | 0.064886 | 0.054811 | -0.050364 | -0.038740 | 0.061453 | 0.062021 | ... | -0.074181 | 0.074304 | -0.082864 | -0.031436 | 1.000000 | 0.051307 | 0.031946 | 0.023217 | 0.010694 | 0.111447 |
| PoolArea | 0.057044 | 0.008283 | 0.180868 | 0.077672 | 0.065166 | -0.001985 | 0.004950 | 0.005829 | 0.011723 | 0.140491 | ... | 0.073378 | 0.060762 | 0.054203 | -0.007992 | 0.051307 | 1.000000 | 0.029669 | -0.033737 | -0.059689 | 0.092404 |
| MiscVal | -0.006242 | -0.007683 | 0.001168 | 0.038068 | -0.031406 | 0.068777 | -0.034383 | -0.010286 | -0.029815 | 0.003571 | ... | -0.009551 | -0.018584 | 0.018361 | 0.000354 | 0.031946 | 0.029669 | 1.000000 | -0.006495 | 0.004906 | -0.021190 |
| MoSold | 0.021172 | -0.013585 | 0.010158 | 0.001205 | 0.070815 | -0.003511 | 0.012398 | 0.021490 | -0.005940 | -0.015727 | ... | 0.021011 | 0.071255 | -0.028887 | 0.029474 | 0.023217 | -0.033737 | -0.006495 | 1.000000 | -0.145721 | 0.046432 |
| YrSold | 0.000712 | -0.021407 | 0.006768 | -0.014261 | -0.027347 | 0.043950 | -0.013618 | 0.035743 | -0.008184 | 0.014359 | ... | 0.022270 | -0.057619 | -0.009916 | 0.018645 | 0.010694 | -0.059689 | 0.004906 | -0.145721 | 1.000000 | -0.028923 |
| SalePrice | -0.021917 | -0.084284 | 0.334901 | 0.263843 | 0.790982 | -0.077856 | 0.522897 | 0.507101 | 0.475241 | 0.386420 | ... | 0.324413 | 0.315856 | -0.128578 | 0.044584 | 0.111447 | 0.092404 | -0.021190 | 0.046432 | -0.028923 | 1.000000 |
38 rows × 38 columns
sb.heatmap(a,annot=True)
<Axes: >
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
X1=pd.DataFrame(ss.fit_transform(X[con]),columns=con)
X1
| MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | MoSold | YrSold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.073375 | -0.229372 | -0.207142 | 0.651479 | -0.517200 | 1.050994 | 0.878668 | 0.511418 | 0.575425 | -0.288653 | ... | 1.021157 | 0.311725 | 0.351000 | -0.752176 | 0.216503 | -0.359325 | -0.116339 | -0.270208 | -1.599111 | 0.138777 |
| 1 | -0.872563 | 0.451936 | -0.091886 | -0.071836 | 2.179628 | 0.156734 | -0.429577 | -0.574410 | 1.171992 | -0.288653 | ... | -0.104483 | 0.311725 | -0.060731 | 1.626195 | -0.704483 | -0.359325 | -0.116339 | -0.270208 | -0.489110 | -0.614439 |
| 2 | 0.073375 | -0.093110 | 0.073480 | 0.651479 | -0.517200 | 0.984752 | 0.830215 | 0.323060 | 0.092907 | -0.288653 | ... | 0.937776 | 0.311725 | 0.631726 | -0.752176 | -0.070361 | -0.359325 | -0.116339 | -0.270208 | 0.990891 | 0.138777 |
| 3 | 0.309859 | -0.456474 | -0.096897 | 0.651479 | -0.517200 | -1.863632 | -0.720298 | -0.574410 | -0.499274 | -0.288653 | ... | 0.812705 | 1.650307 | 0.790804 | -0.752176 | -0.176048 | 4.092524 | -0.116339 | -0.270208 | -1.599111 | -1.367655 |
| 4 | 0.073375 | 0.633618 | 0.375148 | 1.374795 | -0.517200 | 0.951632 | 0.733308 | 1.364570 | 0.463568 | -0.288653 | ... | 0.896086 | 1.650307 | 1.698485 | 0.780197 | 0.563760 | -0.359325 | -0.116339 | -0.270208 | 2.100892 | 0.138777 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1455 | 0.073375 | -0.365633 | -0.260560 | -0.071836 | -0.517200 | 0.918511 | 0.733308 | -0.574410 | -0.973018 | -0.288653 | ... | 0.854395 | 0.311725 | -0.060731 | -0.752176 | -0.100558 | -0.359325 | -0.116339 | -0.270208 | 0.620891 | -0.614439 |
| 1456 | -0.872563 | 0.679039 | 0.266407 | -0.071836 | 0.381743 | 0.222975 | 0.151865 | 0.084843 | 0.759659 | 0.722112 | ... | -0.021102 | 0.311725 | 0.126420 | 2.033231 | -0.704483 | -0.359325 | -0.116339 | -0.270208 | -1.599111 | 1.645210 |
| 1457 | 0.309859 | -0.183951 | -0.147810 | 0.651479 | 3.078570 | -1.002492 | 1.024029 | -0.574410 | -0.369871 | -0.288653 | ... | -1.563645 | -1.026858 | -1.033914 | -0.752176 | 0.201405 | -0.359325 | -0.116339 | -0.270208 | -0.489110 | 1.645210 |
| 1458 | -0.872563 | -0.093110 | -0.080160 | -0.795151 | 0.381743 | -0.704406 | 0.539493 | -0.574410 | -0.865548 | 6.092188 | ... | -1.188432 | -1.026858 | -1.090059 | 2.168910 | -0.704483 | 1.473789 | -0.116339 | -0.270208 | -0.859110 | 1.645210 |
| 1459 | -0.872563 | 0.224833 | -0.058112 | -0.795151 | 0.381743 | -0.207594 | -0.962566 | -0.574410 | 0.847389 | 1.509640 | ... | -0.563077 | -1.026858 | -0.921624 | 5.121921 | 0.322190 | -0.359325 | -0.116339 | -0.270208 | -0.119110 | 0.138777 |
1460 rows × 33 columns
X2=pd.get_dummies(X[cat])
X2
| MSZoning_C (all) | MSZoning_FV | MSZoning_RH | MSZoning_RL | MSZoning_RM | Street_Grvl | Street_Pave | LotShape_IR1 | LotShape_IR2 | LotShape_IR3 | ... | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1455 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1456 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1457 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1458 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1459 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
1460 rows × 243 columns
X2.columns
Index(['MSZoning_C (all)', 'MSZoning_FV', 'MSZoning_RH', 'MSZoning_RL',
'MSZoning_RM', 'Street_Grvl', 'Street_Pave', 'LotShape_IR1',
'LotShape_IR2', 'LotShape_IR3',
...
'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
'SaleCondition_Partial'],
dtype='object', length=243)
df.SaleCondition.value_counts()
Normal 1198 Partial 125 Abnorml 101 Family 20 Alloca 12 AdjLand 4 Name: SaleCondition, dtype: int64
out=[]
for i in con:
out.extend((X1[(X1[i]>3) | (X1[i]<-3)]).index)
print(out)
[9, 48, 93, 125, 165, 246, 291, 300, 312, 335, 411, 488, 520, 535, 635, 637, 703, 705, 713, 861, 969, 985, 1030, 1062, 1144, 1186, 1190, 1266, 1393, 1416, 171, 197, 231, 277, 313, 446, 807, 909, 934, 1107, 1127, 1173, 1182, 1211, 1298, 1337, 53, 249, 313, 335, 384, 451, 457, 661, 706, 769, 848, 1298, 1396, 375, 533, 88, 185, 191, 218, 241, 250, 304, 375, 378, 398, 461, 508, 519, 583, 676, 703, 726, 745, 980, 991, 1123, 1149, 1213, 1268, 1327, 1352, 1435, 1457, 304, 630, 747, 1132, 1137, 1349, 37, 58, 70, 105, 115, 161, 169, 178, 224, 297, 349, 403, 477, 517, 523, 654, 691, 718, 755, 763, 798, 808, 825, 898, 981, 1111, 1169, 1228, 1289, 1298, 1373, 1417, 70, 178, 523, 898, 1182, 1298, 24, 52, 113, 116, 153, 166, 233, 253, 260, 263, 271, 273, 313, 322, 355, 414, 440, 446, 470, 493, 542, 548, 577, 586, 599, 666, 697, 764, 785, 828, 842, 854, 888, 918, 923, 924, 1040, 1059, 1077, 1152, 1220, 1253, 1299, 1308, 1320, 1369, 1387, 1418, 1445, 1458, 137, 224, 278, 477, 496, 581, 678, 774, 798, 932, 1267, 224, 332, 440, 496, 523, 691, 1044, 1182, 1298, 1373, 224, 440, 496, 523, 529, 691, 898, 1024, 1044, 1182, 1298, 1373, 304, 691, 1169, 1182, 118, 185, 197, 304, 496, 523, 608, 635, 691, 769, 798, 1169, 1182, 1268, 1298, 1353, 53, 188, 313, 326, 335, 420, 454, 588, 634, 738, 807, 921, 942, 1163, 1270, 1298, 1, 26, 33, 37, 41, 50, 93, 116, 129, 176, 197, 201, 213, 215, 218, 245, 249, 251, 253, 298, 299, 314, 330, 352, 358, 367, 414, 421, 426, 499, 504, 558, 574, 576, 580, 597, 611, 628, 633, 658, 691, 697, 717, 741, 743, 745, 814, 828, 892, 920, 925, 931, 944, 952, 953, 954, 1006, 1029, 1041, 1047, 1052, 1055, 1069, 1072, 1076, 1080, 1103, 1118, 1123, 1149, 1156, 1181, 1213, 1225, 1276, 1287, 1327, 1335, 1350, 1389, 1405, 1415, 188, 298, 597, 624, 628, 921, 1154, 1163, 1230, 1283, 1350, 1450, 53, 144, 189, 291, 330, 570, 634, 635, 843, 897, 1163, 1213, 1270, 1350, 8, 9, 17, 39, 48, 74, 78, 93, 102, 137, 144, 165, 188, 246, 330, 342, 420, 441, 454, 488, 505, 520, 529, 570, 634, 635, 637, 676, 703, 705, 728, 736, 778, 809, 843, 886, 894, 897, 910, 913, 921, 940, 942, 943, 954, 955, 984, 1003, 1011, 1030, 1062, 1090, 1163, 1186, 1216, 1230, 1232, 1266, 1275, 1283, 1292, 1336, 1350, 1391, 1393, 1412, 1416, 1450, 185, 635, 769, 803, 897, 910, 1031, 1173, 1230, 1298, 1350, 1386, 166, 309, 605, 642, 1298, 93, 653, 178, 581, 664, 825, 1061, 1190, 1298, 53, 64, 166, 169, 335, 343, 357, 480, 661, 769, 828, 848, 893, 961, 974, 1044, 1068, 1210, 1312, 1313, 1423, 1459, 28, 185, 293, 495, 499, 523, 583, 591, 645, 664, 666, 713, 735, 775, 784, 807, 854, 875, 947, 961, 996, 1184, 1193, 1292, 1298, 1328, 1369, 3, 7, 154, 197, 260, 306, 314, 325, 328, 358, 365, 380, 459, 462, 496, 520, 577, 630, 648, 653, 660, 662, 718, 720, 747, 799, 813, 836, 840, 918, 939, 945, 1013, 1030, 1081, 1119, 1139, 1150, 1152, 1185, 1197, 1202, 1248, 1266, 1326, 1360, 1382, 1393, 1419, 1439, 1445, 5, 55, 120, 129, 159, 182, 187, 205, 237, 258, 280, 546, 704, 726, 744, 889, 941, 1080, 1156, 1161, 1181, 1346, 1437, 46, 72, 80, 104, 176, 185, 189, 196, 289, 297, 312, 339, 351, 359, 360, 366, 400, 426, 471, 475, 550, 605, 618, 625, 647, 673, 764, 769, 785, 795, 803, 828, 830, 854, 859, 887, 888, 907, 919, 944, 1037, 1055, 1067, 1070, 1154, 1171, 1184, 1228, 1282, 1293, 1301, 1320, 1328, 1386, 1414]
import numpy as np
outliers=np.unique(out)
outliers
array([ 1, 3, 5, 7, 8, 9, 17, 24, 26, 28, 33,
37, 39, 41, 46, 48, 50, 52, 53, 55, 58, 64,
70, 72, 74, 78, 80, 88, 93, 102, 104, 105, 113,
115, 116, 118, 120, 125, 129, 137, 144, 153, 154, 159,
161, 165, 166, 169, 171, 176, 178, 182, 185, 187, 188,
189, 191, 196, 197, 201, 205, 213, 215, 218, 224, 231,
233, 237, 241, 245, 246, 249, 250, 251, 253, 258, 260,
263, 271, 273, 277, 278, 280, 289, 291, 293, 297, 298,
299, 300, 304, 306, 309, 312, 313, 314, 322, 325, 326,
328, 330, 332, 335, 339, 342, 343, 349, 351, 352, 355,
357, 358, 359, 360, 365, 366, 367, 375, 378, 380, 384,
398, 400, 403, 411, 414, 420, 421, 426, 440, 441, 446,
451, 454, 457, 459, 461, 462, 470, 471, 475, 477, 480,
488, 493, 495, 496, 499, 504, 505, 508, 517, 519, 520,
523, 529, 533, 535, 542, 546, 548, 550, 558, 570, 574,
576, 577, 580, 581, 583, 586, 588, 591, 597, 599, 605,
608, 611, 618, 624, 625, 628, 630, 633, 634, 635, 637,
642, 645, 647, 648, 653, 654, 658, 660, 661, 662, 664,
666, 673, 676, 678, 691, 697, 703, 704, 705, 706, 713,
717, 718, 720, 726, 728, 735, 736, 738, 741, 743, 744,
745, 747, 755, 763, 764, 769, 774, 775, 778, 784, 785,
795, 798, 799, 803, 807, 808, 809, 813, 814, 825, 828,
830, 836, 840, 842, 843, 848, 854, 859, 861, 875, 886,
887, 888, 889, 892, 893, 894, 897, 898, 907, 909, 910,
913, 918, 919, 920, 921, 923, 924, 925, 931, 932, 934,
939, 940, 941, 942, 943, 944, 945, 947, 952, 953, 954,
955, 961, 969, 974, 980, 981, 984, 985, 991, 996, 1003,
1006, 1011, 1013, 1024, 1029, 1030, 1031, 1037, 1040, 1041, 1044,
1047, 1052, 1055, 1059, 1061, 1062, 1067, 1068, 1069, 1070, 1072,
1076, 1077, 1080, 1081, 1090, 1103, 1107, 1111, 1118, 1119, 1123,
1127, 1132, 1137, 1139, 1144, 1149, 1150, 1152, 1154, 1156, 1161,
1163, 1169, 1171, 1173, 1181, 1182, 1184, 1185, 1186, 1190, 1193,
1197, 1202, 1210, 1211, 1213, 1216, 1220, 1225, 1228, 1230, 1232,
1248, 1253, 1266, 1267, 1268, 1270, 1275, 1276, 1282, 1283, 1287,
1289, 1292, 1293, 1298, 1299, 1301, 1308, 1312, 1313, 1320, 1326,
1327, 1328, 1335, 1336, 1337, 1346, 1349, 1350, 1352, 1353, 1360,
1369, 1373, 1382, 1386, 1387, 1389, 1391, 1393, 1396, 1405, 1412,
1414, 1415, 1416, 1417, 1418, 1419, 1423, 1435, 1437, 1439, 1445,
1450, 1457, 1458, 1459])
X1.drop(index=outliers,axis=0,inplace=True)
X2.drop(index=outliers,axis=0,inplace=True)
Y.drop(index=outliers,axis=0,inplace=True)
X1.shape
(1038, 33)
X2.shape
(1038, 243)
Y.shape
(1038,)
Xnew=X1.join(X2)
Xnew.shape
(1038, 276)
Xnew.index=range(0,1038)
Y.index=range(0,1038)
Xnew.head()
| MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.073375 | -0.229372 | -0.207142 | 0.651479 | -0.5172 | 1.050994 | 0.878668 | 0.511418 | 0.575425 | -0.288653 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 0.073375 | -0.093110 | 0.073480 | 0.651479 | -0.5172 | 0.984752 | 0.830215 | 0.323060 | 0.092907 | -0.288653 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 0.073375 | 0.633618 | 0.375148 | 1.374795 | -0.5172 | 0.951632 | 0.733308 | 1.364570 | 0.463568 | -0.288653 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | -0.872563 | 0.224833 | -0.043379 | 1.374795 | -0.5172 | 1.084115 | 0.975575 | 0.456019 | 2.029558 | -0.288653 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4 | -0.872563 | -0.002269 | 0.068469 | -0.795151 | -0.5172 | -0.207594 | -0.962566 | -0.574410 | 1.014077 | -0.288653 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
5 rows × 276 columns
Xnew.columns
Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
...
'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
'SaleCondition_Partial'],
dtype='object', length=276)
from sklearn.model_selection import train_test_split
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.3,random_state=54)
xtrain.shape
(726, 276)
xtrain.columns
Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
...
'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
'SaleCondition_Partial'],
dtype='object', length=276)
ytrain.shape
(726,)
ytrain.info()
<class 'pandas.core.series.Series'> Int64Index: 726 entries, 24 to 898 Series name: SalePrice Non-Null Count Dtype -------------- ----- 726 non-null int64 dtypes: int64(1) memory usage: 11.3 KB
from statsmodels.api import OLS,add_constant
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
model.summary()
| Dep. Variable: | SalePrice | R-squared: | 0.953 |
|---|---|---|---|
| Model: | OLS | Adj. R-squared: | 0.935 |
| Method: | Least Squares | F-statistic: | 52.82 |
| Date: | Sat, 26 Aug 2023 | Prob (F-statistic): | 1.05e-259 |
| Time: | 11:22:09 | Log-Likelihood: | -7964.7 |
| No. Observations: | 726 | AIC: | 1.633e+04 |
| Df Residuals: | 525 | BIC: | 1.725e+04 |
| Df Model: | 200 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| MSSubClass | -1.315e+04 | 6547.419 | -2.009 | 0.045 | -2.6e+04 | -290.031 |
| LotFrontage | -260.4685 | 1314.618 | -0.198 | 0.843 | -2843.026 | 2322.089 |
| LotArea | 1.287e+04 | 3440.242 | 3.741 | 0.000 | 6110.629 | 1.96e+04 |
| OverallQual | 8283.9104 | 1678.468 | 4.935 | 0.000 | 4986.571 | 1.16e+04 |
| OverallCond | 5896.5633 | 1172.848 | 5.028 | 0.000 | 3592.512 | 8200.614 |
| YearBuilt | 9837.1789 | 3004.526 | 3.274 | 0.001 | 3934.810 | 1.57e+04 |
| YearRemodAdd | 1206.9199 | 1408.688 | 0.857 | 0.392 | -1560.438 | 3974.277 |
| MasVnrArea | 1473.3009 | 1670.927 | 0.882 | 0.378 | -1809.224 | 4755.825 |
| BsmtFinSF1 | 6388.6517 | 1281.327 | 4.986 | 0.000 | 3871.493 | 8905.810 |
| BsmtFinSF2 | 5896.6564 | 3122.203 | 1.889 | 0.059 | -236.889 | 1.2e+04 |
| BsmtUnfSF | -33.5562 | 1076.981 | -0.031 | 0.975 | -2149.277 | 2082.164 |
| TotalBsmtSF | 8776.4351 | 1717.973 | 5.109 | 0.000 | 5401.489 | 1.22e+04 |
| 1stFlrSF | 4766.9636 | 1.26e+04 | 0.378 | 0.705 | -2e+04 | 2.95e+04 |
| 2ndFlrSF | 1.122e+04 | 1.38e+04 | 0.816 | 0.415 | -1.58e+04 | 3.82e+04 |
| GrLivArea | 1.709e+04 | 1.7e+04 | 1.007 | 0.314 | -1.63e+04 | 5.04e+04 |
| BsmtFullBath | 397.9571 | 1174.296 | 0.339 | 0.735 | -1908.940 | 2704.854 |
| BsmtHalfBath | -3884.5450 | 573.727 | -6.771 | 0.000 | -5011.627 | -2757.463 |
| FullBath | 4312.3842 | 1546.375 | 2.789 | 0.005 | 1274.542 | 7350.226 |
| HalfBath | 1391.3756 | 1315.070 | 1.058 | 0.291 | -1192.070 | 3974.821 |
| BedroomAbvGr | -913.1800 | 1426.761 | -0.640 | 0.522 | -3716.042 | 1889.682 |
| KitchenAbvGr | -3407.4397 | 503.261 | -6.771 | 0.000 | -4396.092 | -2418.788 |
| TotRmsAbvGrd | -1036.6683 | 1827.880 | -0.567 | 0.571 | -4627.526 | 2554.189 |
| Fireplaces | 3305.4939 | 1186.072 | 2.787 | 0.006 | 975.464 | 5635.523 |
| GarageYrBlt | -262.5252 | 1552.134 | -0.169 | 0.866 | -3311.682 | 2786.631 |
| GarageCars | 2592.5982 | 1886.022 | 1.375 | 0.170 | -1112.478 | 6297.675 |
| GarageArea | 3241.3391 | 1964.675 | 1.650 | 0.100 | -618.251 | 7100.929 |
| WoodDeckSF | 4139.0441 | 939.410 | 4.406 | 0.000 | 2293.580 | 5984.508 |
| OpenPorchSF | 1364.6058 | 1117.353 | 1.221 | 0.223 | -830.427 | 3559.638 |
| EnclosedPorch | 314.8829 | 1235.887 | 0.255 | 0.799 | -2113.009 | 2742.775 |
| 3SsnPorch | -1.9e+04 | 2.3e+04 | -0.827 | 0.409 | -6.41e+04 | 2.61e+04 |
| ScreenPorch | 357.1218 | 1436.578 | 0.249 | 0.804 | -2465.025 | 3179.268 |
| MoSold | 104.2274 | 756.613 | 0.138 | 0.890 | -1382.133 | 1590.587 |
| YrSold | 140.5732 | 775.208 | 0.181 | 0.856 | -1382.318 | 1663.464 |
| MSZoning_C (all) | -1.973e+04 | 9540.011 | -2.068 | 0.039 | -3.85e+04 | -986.224 |
| MSZoning_FV | 2.463e+04 | 6400.234 | 3.848 | 0.000 | 1.21e+04 | 3.72e+04 |
| MSZoning_RH | -4637.8667 | 7896.246 | -0.587 | 0.557 | -2.01e+04 | 1.09e+04 |
| MSZoning_RL | 4407.9207 | 3951.065 | 1.116 | 0.265 | -3353.917 | 1.22e+04 |
| MSZoning_RM | 1.145e+04 | 4259.858 | 2.687 | 0.007 | 3078.333 | 1.98e+04 |
| Street_Grvl | 1.687e+04 | 9491.563 | 1.777 | 0.076 | -1777.977 | 3.55e+04 |
| Street_Pave | -753.7680 | 8763.872 | -0.086 | 0.931 | -1.8e+04 | 1.65e+04 |
| LotShape_IR1 | 5562.8497 | 3350.516 | 1.660 | 0.097 | -1019.216 | 1.21e+04 |
| LotShape_IR2 | 4869.2742 | 5034.150 | 0.967 | 0.334 | -5020.277 | 1.48e+04 |
| LotShape_IR3 | -242.9744 | 8289.449 | -0.029 | 0.977 | -1.65e+04 | 1.6e+04 |
| LotShape_Reg | 5925.2134 | 3525.365 | 1.681 | 0.093 | -1000.340 | 1.29e+04 |
| LandContour_Bnk | 7995.1203 | 4274.066 | 1.871 | 0.062 | -401.252 | 1.64e+04 |
| LandContour_HLS | 1.781e+04 | 4086.966 | 4.358 | 0.000 | 9784.082 | 2.58e+04 |
| LandContour_Low | -1.705e+04 | 5523.692 | -3.086 | 0.002 | -2.79e+04 | -6195.030 |
| LandContour_Lvl | 7352.6291 | 2642.968 | 2.782 | 0.006 | 2160.536 | 1.25e+04 |
| Utilities_AllPub | 1.611e+04 | 2380.005 | 6.771 | 0.000 | 1.14e+04 | 2.08e+04 |
| Utilities_NoSeWa | -1.609e-10 | 6e-11 | -2.682 | 0.008 | -2.79e-10 | -4.3e-11 |
| LotConfig_Corner | 8365.4851 | 3655.809 | 2.288 | 0.023 | 1183.675 | 1.55e+04 |
| LotConfig_CulDSac | 1.133e+04 | 4135.856 | 2.739 | 0.006 | 3205.118 | 1.95e+04 |
| LotConfig_FR2 | -1040.4242 | 4513.464 | -0.231 | 0.818 | -9907.092 | 7826.243 |
| LotConfig_FR3 | -1.071e+04 | 1.27e+04 | -0.845 | 0.398 | -3.56e+04 | 1.42e+04 |
| LotConfig_Inside | 8166.3588 | 3455.492 | 2.363 | 0.018 | 1378.070 | 1.5e+04 |
| LandSlope_Gtl | 1.0049 | 6236.634 | 0.000 | 1.000 | -1.23e+04 | 1.23e+04 |
| LandSlope_Mod | -2274.7517 | 6781.438 | -0.335 | 0.737 | -1.56e+04 | 1.1e+04 |
| LandSlope_Sev | 1.839e+04 | 1.26e+04 | 1.454 | 0.146 | -6448.529 | 4.32e+04 |
| Neighborhood_Blmngtn | 2.253e+04 | 7115.645 | 3.166 | 0.002 | 8549.491 | 3.65e+04 |
| Neighborhood_Blueste | -6630.9441 | 1.79e+04 | -0.370 | 0.712 | -4.18e+04 | 2.86e+04 |
| Neighborhood_BrDale | 2551.2707 | 9232.445 | 0.276 | 0.782 | -1.56e+04 | 2.07e+04 |
| Neighborhood_BrkSide | -2815.8276 | 5833.370 | -0.483 | 0.630 | -1.43e+04 | 8643.786 |
| Neighborhood_ClearCr | 3044.3355 | 7729.911 | 0.394 | 0.694 | -1.21e+04 | 1.82e+04 |
| Neighborhood_CollgCr | -4170.0792 | 3149.905 | -1.324 | 0.186 | -1.04e+04 | 2017.887 |
| Neighborhood_Crawfor | 2.082e+04 | 6436.559 | 3.234 | 0.001 | 8172.909 | 3.35e+04 |
| Neighborhood_Edwards | -9957.5330 | 3757.448 | -2.650 | 0.008 | -1.73e+04 | -2576.053 |
| Neighborhood_Gilbert | -6617.4477 | 4085.241 | -1.620 | 0.106 | -1.46e+04 | 1407.980 |
| Neighborhood_IDOTRR | -8494.0362 | 7401.705 | -1.148 | 0.252 | -2.3e+04 | 6046.561 |
| Neighborhood_MeadowV | -1.155e+04 | 8766.009 | -1.318 | 0.188 | -2.88e+04 | 5668.167 |
| Neighborhood_Mitchel | -8888.8204 | 4481.737 | -1.983 | 0.048 | -1.77e+04 | -84.481 |
| Neighborhood_NAmes | -8133.5656 | 3157.561 | -2.576 | 0.010 | -1.43e+04 | -1930.559 |
| Neighborhood_NPkVill | 1.612e+04 | 1.15e+04 | 1.401 | 0.162 | -6483.589 | 3.87e+04 |
| Neighborhood_NWAmes | -1.505e+04 | 4147.915 | -3.628 | 0.000 | -2.32e+04 | -6900.090 |
| Neighborhood_NoRidge | 1.219e+04 | 5690.954 | 2.141 | 0.033 | 1005.287 | 2.34e+04 |
| Neighborhood_NridgHt | 1.388e+04 | 4827.917 | 2.874 | 0.004 | 4392.860 | 2.34e+04 |
| Neighborhood_OldTown | -1.704e+04 | 5502.935 | -3.097 | 0.002 | -2.79e+04 | -6234.508 |
| Neighborhood_SWISU | -1.057e+04 | 8678.849 | -1.218 | 0.224 | -2.76e+04 | 6480.937 |
| Neighborhood_Sawyer | -6521.8028 | 3885.752 | -1.678 | 0.094 | -1.42e+04 | 1111.730 |
| Neighborhood_SawyerW | -5028.4263 | 4413.136 | -1.139 | 0.255 | -1.37e+04 | 3641.148 |
| Neighborhood_Somerst | -7246.4231 | 6273.337 | -1.155 | 0.249 | -1.96e+04 | 5077.502 |
| Neighborhood_StoneBr | 6.023e+04 | 6716.782 | 8.967 | 0.000 | 4.7e+04 | 7.34e+04 |
| Neighborhood_Timber | -7879.7337 | 5016.435 | -1.571 | 0.117 | -1.77e+04 | 1975.016 |
| Neighborhood_Veenker | 1354.9000 | 1.43e+04 | 0.095 | 0.924 | -2.67e+04 | 2.94e+04 |
| Condition1_Artery | -9465.7000 | 5654.475 | -1.674 | 0.095 | -2.06e+04 | 1642.475 |
| Condition1_Feedr | -23.5864 | 4896.137 | -0.005 | 0.996 | -9642.012 | 9594.839 |
| Condition1_Norm | 3728.5999 | 3618.612 | 1.030 | 0.303 | -3380.138 | 1.08e+04 |
| Condition1_PosA | 1.414e+04 | 1.26e+04 | 1.121 | 0.263 | -1.06e+04 | 3.89e+04 |
| Condition1_PosN | 9318.8611 | 9672.546 | 0.963 | 0.336 | -9682.786 | 2.83e+04 |
| Condition1_RRAe | -1.35e+04 | 8214.265 | -1.643 | 0.101 | -2.96e+04 | 2640.356 |
| Condition1_RRAn | -85.4550 | 5575.391 | -0.015 | 0.988 | -1.1e+04 | 1.09e+04 |
| Condition1_RRNe | -2254.3990 | 1.59e+04 | -0.142 | 0.887 | -3.35e+04 | 2.9e+04 |
| Condition1_RRNn | 1.425e+04 | 1.46e+04 | 0.977 | 0.329 | -1.44e+04 | 4.29e+04 |
| Condition2_Artery | 1.231e-12 | 2.61e-11 | 0.047 | 0.962 | -5.01e-11 | 5.26e-11 |
| Condition2_Feedr | 5064.5926 | 8192.456 | 0.618 | 0.537 | -1.1e+04 | 2.12e+04 |
| Condition2_Norm | 1.105e+04 | 7675.804 | 1.440 | 0.151 | -4029.291 | 2.61e+04 |
| Condition2_PosA | 2.996e-11 | 3.19e-11 | 0.939 | 0.348 | -3.27e-11 | 9.26e-11 |
| Condition2_PosN | 4.697e-11 | 2.92e-11 | 1.610 | 0.108 | -1.03e-11 | 1.04e-10 |
| Condition2_RRAe | -6.856e-11 | 3.87e-11 | -1.771 | 0.077 | -1.45e-10 | 7.49e-12 |
| Condition2_RRAn | -8.321e-11 | 3.52e-11 | -2.366 | 0.018 | -1.52e-10 | -1.41e-11 |
| Condition2_RRNn | -1.806e-11 | 2.52e-11 | -0.716 | 0.474 | -6.76e-11 | 3.15e-11 |
| BldgType_1Fam | 7115.4212 | 8702.688 | 0.818 | 0.414 | -9980.947 | 2.42e+04 |
| BldgType_2fmCon | 3026.8797 | 2.22e+04 | 0.136 | 0.892 | -4.06e+04 | 4.67e+04 |
| BldgType_Duplex | -1.296e+04 | 1.21e+04 | -1.070 | 0.285 | -3.67e+04 | 1.08e+04 |
| BldgType_Twnhs | 7282.8735 | 1.12e+04 | 0.649 | 0.516 | -1.47e+04 | 2.93e+04 |
| BldgType_TwnhsE | 1.165e+04 | 1.07e+04 | 1.091 | 0.276 | -9327.572 | 3.26e+04 |
| HouseStyle_1.5Fin | -1694.3085 | 4813.348 | -0.352 | 0.725 | -1.12e+04 | 7761.479 |
| HouseStyle_1.5Unf | 7281.7269 | 8259.900 | 0.882 | 0.378 | -8944.788 | 2.35e+04 |
| HouseStyle_1Story | -1555.9546 | 7211.536 | -0.216 | 0.829 | -1.57e+04 | 1.26e+04 |
| HouseStyle_2.5Fin | -1.623e+04 | 1.77e+04 | -0.915 | 0.361 | -5.11e+04 | 1.86e+04 |
| HouseStyle_2.5Unf | 2709.1949 | 1.79e+04 | 0.151 | 0.880 | -3.25e+04 | 3.79e+04 |
| HouseStyle_2Story | 1073.1293 | 4785.761 | 0.224 | 0.823 | -8328.464 | 1.05e+04 |
| HouseStyle_SFoyer | 1.488e+04 | 7677.669 | 1.938 | 0.053 | -207.154 | 3e+04 |
| HouseStyle_SLvl | 9658.0611 | 6397.015 | 1.510 | 0.132 | -2908.828 | 2.22e+04 |
| RoofStyle_Flat | -4160.3395 | 8628.687 | -0.482 | 0.630 | -2.11e+04 | 1.28e+04 |
| RoofStyle_Gable | -1934.3417 | 5113.683 | -0.378 | 0.705 | -1.2e+04 | 8111.452 |
| RoofStyle_Gambrel | 7143.8783 | 9375.612 | 0.762 | 0.446 | -1.13e+04 | 2.56e+04 |
| RoofStyle_Hip | 1134.1691 | 5272.631 | 0.215 | 0.830 | -9223.877 | 1.15e+04 |
| RoofStyle_Mansard | 1.393e+04 | 1.09e+04 | 1.280 | 0.201 | -7454.425 | 3.53e+04 |
| RoofStyle_Shed | 8.508e-12 | 1.93e-11 | 0.440 | 0.660 | -2.95e-11 | 4.65e-11 |
| RoofMatl_ClyTile | -1.77e-12 | 2.31e-11 | -0.077 | 0.939 | -4.71e-11 | 4.36e-11 |
| RoofMatl_CompShg | 2.619e+04 | 1.14e+04 | 2.299 | 0.022 | 3806.595 | 4.86e+04 |
| RoofMatl_Membran | -2.037e-11 | 2.15e-11 | -0.947 | 0.344 | -6.26e-11 | 2.19e-11 |
| RoofMatl_Metal | 3.193e-11 | 2.53e-11 | 1.264 | 0.207 | -1.77e-11 | 8.16e-11 |
| RoofMatl_Roll | 9.673e-12 | 2.42e-11 | 0.400 | 0.690 | -3.79e-11 | 5.72e-11 |
| RoofMatl_Tar&Grv | -4160.3395 | 8628.687 | -0.482 | 0.630 | -2.11e+04 | 1.28e+04 |
| RoofMatl_WdShake | -5920.0443 | 1.59e+04 | -0.372 | 0.710 | -3.72e+04 | 2.54e+04 |
| RoofMatl_WdShngl | 2.498e-11 | 2.41e-11 | 1.037 | 0.300 | -2.23e-11 | 7.23e-11 |
| Exterior1st_AsbShng | 2.129e+04 | 1.67e+04 | 1.273 | 0.204 | -1.16e+04 | 5.41e+04 |
| Exterior1st_AsphShn | 2.522e-12 | 2.05e-11 | 0.123 | 0.902 | -3.77e-11 | 4.27e-11 |
| Exterior1st_BrkComm | -1807.4964 | 2.49e+04 | -0.073 | 0.942 | -5.06e+04 | 4.7e+04 |
| Exterior1st_BrkFace | 1.658e+04 | 7458.438 | 2.224 | 0.027 | 1932.197 | 3.12e+04 |
| Exterior1st_CBlock | 1.7e+04 | 1.18e+04 | 1.437 | 0.151 | -6241.513 | 4.02e+04 |
| Exterior1st_CemntBd | 4775.5565 | 2889.020 | 1.653 | 0.099 | -899.902 | 1.05e+04 |
| Exterior1st_HdBoard | 1167.4066 | 6205.911 | 0.188 | 0.851 | -1.1e+04 | 1.34e+04 |
| Exterior1st_ImStucc | 8.55e-12 | 1.35e-11 | 0.631 | 0.528 | -1.81e-11 | 3.52e-11 |
| Exterior1st_MetalSd | 3080.1012 | 1.03e+04 | 0.300 | 0.764 | -1.71e+04 | 2.32e+04 |
| Exterior1st_Plywood | 6258.6768 | 6368.168 | 0.983 | 0.326 | -6251.544 | 1.88e+04 |
| Exterior1st_Stone | 1.25e-11 | 1.42e-11 | 0.882 | 0.378 | -1.53e-11 | 4.03e-11 |
| Exterior1st_Stucco | -5.726e+04 | 2.2e+04 | -2.605 | 0.009 | -1e+05 | -1.41e+04 |
| Exterior1st_VinylSd | -1437.8909 | 8293.849 | -0.173 | 0.862 | -1.77e+04 | 1.49e+04 |
| Exterior1st_Wd Sdng | -1609.8616 | 5957.645 | -0.270 | 0.787 | -1.33e+04 | 1.01e+04 |
| Exterior1st_WdShing | 8077.2753 | 7596.801 | 1.063 | 0.288 | -6846.587 | 2.3e+04 |
| Exterior2nd_AsbShng | -5201.2122 | 1.56e+04 | -0.333 | 0.739 | -3.59e+04 | 2.55e+04 |
| Exterior2nd_AsphShn | -7.205e-13 | 1.72e-11 | -0.042 | 0.967 | -3.44e-11 | 3.3e-11 |
| Exterior2nd_Brk Cmn | -7196.0535 | 1.39e+04 | -0.518 | 0.605 | -3.45e+04 | 2.01e+04 |
| Exterior2nd_BrkFace | -2.015e+04 | 9836.586 | -2.048 | 0.041 | -3.95e+04 | -823.243 |
| Exterior2nd_CBlock | 1.7e+04 | 1.18e+04 | 1.437 | 0.151 | -6241.513 | 4.02e+04 |
| Exterior2nd_CmentBd | 4775.5565 | 2889.020 | 1.653 | 0.099 | -899.902 | 1.05e+04 |
| Exterior2nd_HdBoard | -5843.1708 | 5825.026 | -1.003 | 0.316 | -1.73e+04 | 5600.052 |
| Exterior2nd_ImStucc | -2.62e+04 | 1.19e+04 | -2.195 | 0.029 | -4.96e+04 | -2746.387 |
| Exterior2nd_MetalSd | 4151.3490 | 1.01e+04 | 0.411 | 0.682 | -1.57e+04 | 2.4e+04 |
| Exterior2nd_Other | -1.141e-11 | 8.83e-12 | -1.293 | 0.197 | -2.88e-11 | 5.93e-12 |
| Exterior2nd_Plywood | -8516.1279 | 5095.901 | -1.671 | 0.095 | -1.85e+04 | 1494.733 |
| Exterior2nd_Stone | -1.007e-11 | 1.14e-11 | -0.884 | 0.377 | -3.24e-11 | 1.23e-11 |
| Exterior2nd_Stucco | 6.631e+04 | 2.13e+04 | 3.115 | 0.002 | 2.45e+04 | 1.08e+05 |
| Exterior2nd_VinylSd | 1599.1743 | 8152.917 | 0.196 | 0.845 | -1.44e+04 | 1.76e+04 |
| Exterior2nd_Wd Sdng | 4225.9916 | 5722.357 | 0.739 | 0.461 | -7015.539 | 1.55e+04 |
| Exterior2nd_Wd Shng | -8847.1711 | 6991.072 | -1.265 | 0.206 | -2.26e+04 | 4886.739 |
| MasVnrType_BrkCmn | -2036.8548 | 7386.709 | -0.276 | 0.783 | -1.65e+04 | 1.25e+04 |
| MasVnrType_BrkFace | 2150.7161 | 2787.011 | 0.772 | 0.441 | -3324.346 | 7625.779 |
| MasVnrType_None | 1705.0586 | 3063.321 | 0.557 | 0.578 | -4312.813 | 7722.930 |
| MasVnrType_Stone | 1.43e+04 | 3308.295 | 4.321 | 0.000 | 7796.321 | 2.08e+04 |
| ExterQual_Ex | 1.619e+04 | 6489.369 | 2.495 | 0.013 | 3443.391 | 2.89e+04 |
| ExterQual_Fa | -4122.5778 | 1.16e+04 | -0.354 | 0.723 | -2.7e+04 | 1.87e+04 |
| ExterQual_Gd | 1893.6648 | 4652.527 | 0.407 | 0.684 | -7246.191 | 1.1e+04 |
| ExterQual_TA | 2151.5659 | 4654.361 | 0.462 | 0.644 | -6991.894 | 1.13e+04 |
| ExterCond_Ex | 2.641e-12 | 8.23e-12 | 0.321 | 0.748 | -1.35e-11 | 1.88e-11 |
| ExterCond_Fa | 4131.7192 | 5543.503 | 0.745 | 0.456 | -6758.452 | 1.5e+04 |
| ExterCond_Gd | 4368.9766 | 3332.772 | 1.311 | 0.190 | -2178.229 | 1.09e+04 |
| ExterCond_Po | 4.715e-12 | 1.1e-11 | 0.430 | 0.667 | -1.68e-11 | 2.63e-11 |
| ExterCond_TA | 7613.6670 | 2911.494 | 2.615 | 0.009 | 1894.057 | 1.33e+04 |
| Foundation_BrkTil | -6597.1028 | 5249.409 | -1.257 | 0.209 | -1.69e+04 | 3715.323 |
| Foundation_CBlock | -390.9368 | 4583.020 | -0.085 | 0.932 | -9394.246 | 8612.372 |
| Foundation_PConc | 306.7008 | 4702.587 | 0.065 | 0.948 | -8931.498 | 9544.899 |
| Foundation_Slab | 3338.3849 | 8075.445 | 0.413 | 0.679 | -1.25e+04 | 1.92e+04 |
| Foundation_Stone | 1.014e+04 | 1.27e+04 | 0.800 | 0.424 | -1.47e+04 | 3.5e+04 |
| Foundation_Wood | 9318.1816 | 1.73e+04 | 0.539 | 0.590 | -2.47e+04 | 4.33e+04 |
| BsmtQual_Ex | 9907.7574 | 3547.356 | 2.793 | 0.005 | 2939.002 | 1.69e+04 |
| BsmtQual_Fa | 1.018e+04 | 4531.121 | 2.246 | 0.025 | 1277.498 | 1.91e+04 |
| BsmtQual_Gd | -3212.5523 | 2376.286 | -1.352 | 0.177 | -7880.748 | 1455.644 |
| BsmtQual_TA | -759.6953 | 2383.564 | -0.319 | 0.750 | -5442.191 | 3922.800 |
| BsmtCond_Fa | 383.9748 | 3881.280 | 0.099 | 0.921 | -7240.772 | 8008.722 |
| BsmtCond_Gd | 5359.8455 | 3239.165 | 1.655 | 0.099 | -1003.471 | 1.17e+04 |
| BsmtCond_Po | -1.442e-11 | 7.31e-12 | -1.974 | 0.049 | -2.88e-11 | -7e-14 |
| BsmtCond_TA | 1.037e+04 | 2368.752 | 4.378 | 0.000 | 5717.147 | 1.5e+04 |
| BsmtExposure_Av | 1522.1174 | 1830.707 | 0.831 | 0.406 | -2074.293 | 5118.527 |
| BsmtExposure_Gd | 1.595e+04 | 2774.556 | 5.748 | 0.000 | 1.05e+04 | 2.14e+04 |
| BsmtExposure_Mn | -1265.4973 | 2423.751 | -0.522 | 0.602 | -6026.938 | 3495.943 |
| BsmtExposure_No | -90.3296 | 1655.409 | -0.055 | 0.957 | -3342.368 | 3161.709 |
| BsmtFinType1_ALQ | 4633.0114 | 2010.302 | 2.305 | 0.022 | 683.787 | 8582.236 |
| BsmtFinType1_BLQ | -1012.8177 | 2361.228 | -0.429 | 0.668 | -5651.433 | 3625.798 |
| BsmtFinType1_GLQ | 1.22e+04 | 2134.104 | 5.715 | 0.000 | 8005.000 | 1.64e+04 |
| BsmtFinType1_LwQ | -3944.2561 | 3366.343 | -1.172 | 0.242 | -1.06e+04 | 2668.901 |
| BsmtFinType1_Rec | 1816.3413 | 2575.133 | 0.705 | 0.481 | -3242.488 | 6875.171 |
| BsmtFinType1_Unf | 2424.6528 | 2193.495 | 1.105 | 0.269 | -1884.453 | 6733.758 |
| BsmtFinType2_ALQ | 1.557e+04 | 1.33e+04 | 1.173 | 0.241 | -1.05e+04 | 4.16e+04 |
| BsmtFinType2_BLQ | -1.019e+04 | 5873.208 | -1.736 | 0.083 | -2.17e+04 | 1343.232 |
| BsmtFinType2_GLQ | 1.653e+04 | 9778.485 | 1.690 | 0.092 | -2684.132 | 3.57e+04 |
| BsmtFinType2_LwQ | -4642.6081 | 4903.219 | -0.947 | 0.344 | -1.43e+04 | 4989.730 |
| BsmtFinType2_Rec | -7823.9010 | 5531.896 | -1.414 | 0.158 | -1.87e+04 | 3043.469 |
| BsmtFinType2_Unf | 6677.0985 | 6092.770 | 1.096 | 0.274 | -5292.105 | 1.86e+04 |
| Heating_Floor | 0 | 0 | nan | nan | 0 | 0 |
| Heating_GasA | -6587.0482 | 8170.722 | -0.806 | 0.421 | -2.26e+04 | 9464.277 |
| Heating_GasW | -1.335e+04 | 1.15e+04 | -1.163 | 0.245 | -3.59e+04 | 9201.280 |
| Heating_Grav | 1.403e+04 | 1.4e+04 | 1.002 | 0.317 | -1.35e+04 | 4.15e+04 |
| Heating_OthW | 0 | 0 | nan | nan | 0 | 0 |
| Heating_Wall | 2.202e+04 | 1.67e+04 | 1.316 | 0.189 | -1.09e+04 | 5.49e+04 |
| HeatingQC_Ex | 3958.7241 | 2018.545 | 1.961 | 0.050 | -6.693 | 7924.141 |
| HeatingQC_Fa | 3183.5497 | 4013.857 | 0.793 | 0.428 | -4701.644 | 1.11e+04 |
| HeatingQC_Gd | 4388.2889 | 2102.819 | 2.087 | 0.037 | 257.316 | 8519.262 |
| HeatingQC_Po | 0 | 0 | nan | nan | 0 | 0 |
| HeatingQC_TA | 4583.8002 | 1906.134 | 2.405 | 0.017 | 839.214 | 8328.386 |
| CentralAir_N | 9107.5930 | 2761.543 | 3.298 | 0.001 | 3682.562 | 1.45e+04 |
| CentralAir_Y | 7006.7699 | 2846.041 | 2.462 | 0.014 | 1415.743 | 1.26e+04 |
| Electrical_FuseA | 8272.3678 | 6295.764 | 1.314 | 0.189 | -4095.615 | 2.06e+04 |
| Electrical_FuseF | 953.3996 | 7169.303 | 0.133 | 0.894 | -1.31e+04 | 1.5e+04 |
| Electrical_FuseP | 1879.5518 | 1.73e+04 | 0.109 | 0.914 | -3.21e+04 | 3.59e+04 |
| Electrical_Mix | 0 | 0 | nan | nan | 0 | 0 |
| Electrical_SBrkr | 5009.0436 | 6224.558 | 0.805 | 0.421 | -7219.056 | 1.72e+04 |
| KitchenQual_Ex | 1.796e+04 | 3521.980 | 5.100 | 0.000 | 1.1e+04 | 2.49e+04 |
| KitchenQual_Fa | 272.7772 | 4734.317 | 0.058 | 0.954 | -9027.755 | 9573.309 |
| KitchenQual_Gd | -1020.6401 | 2320.748 | -0.440 | 0.660 | -5579.733 | 3538.453 |
| KitchenQual_TA | -1101.2641 | 2187.552 | -0.503 | 0.615 | -5398.694 | 3196.166 |
| Functional_Maj1 | -5909.9604 | 1.1e+04 | -0.536 | 0.592 | -2.76e+04 | 1.58e+04 |
| Functional_Maj2 | 7311.6884 | 2.09e+04 | 0.351 | 0.726 | -3.37e+04 | 4.83e+04 |
| Functional_Min1 | 2773.6904 | 6744.581 | 0.411 | 0.681 | -1.05e+04 | 1.6e+04 |
| Functional_Min2 | 4538.7546 | 6658.493 | 0.682 | 0.496 | -8541.807 | 1.76e+04 |
| Functional_Mod | -9981.1420 | 9461.133 | -1.055 | 0.292 | -2.86e+04 | 8605.187 |
| Functional_Sev | 0 | 0 | nan | nan | 0 | 0 |
| Functional_Typ | 1.738e+04 | 5017.224 | 3.464 | 0.001 | 7525.031 | 2.72e+04 |
| FireplaceQu_Ex | -1.101e+04 | 7102.582 | -1.550 | 0.122 | -2.5e+04 | 2942.254 |
| FireplaceQu_Fa | -2359.1252 | 4643.678 | -0.508 | 0.612 | -1.15e+04 | 6763.347 |
| FireplaceQu_Gd | 6794.2601 | 2725.762 | 2.493 | 0.013 | 1439.520 | 1.21e+04 |
| FireplaceQu_Po | 1.938e+04 | 5454.613 | 3.553 | 0.000 | 8661.996 | 3.01e+04 |
| FireplaceQu_TA | 3312.3996 | 2772.225 | 1.195 | 0.233 | -2133.616 | 8758.415 |
| GarageType_2Types | 0 | 0 | nan | nan | 0 | 0 |
| GarageType_Attchd | -4931.1936 | 4200.811 | -1.174 | 0.241 | -1.32e+04 | 3321.269 |
| GarageType_Basment | -2229.4607 | 6763.426 | -0.330 | 0.742 | -1.55e+04 | 1.11e+04 |
| GarageType_BuiltIn | -2776.8124 | 5091.721 | -0.545 | 0.586 | -1.28e+04 | 7225.836 |
| GarageType_CarPort | 2.801e+04 | 1.49e+04 | 1.880 | 0.061 | -1251.668 | 5.73e+04 |
| GarageType_Detchd | -1957.7472 | 4342.540 | -0.451 | 0.652 | -1.05e+04 | 6573.141 |
| GarageFinish_Fin | 3640.9994 | 1548.627 | 2.351 | 0.019 | 598.733 | 6683.266 |
| GarageFinish_RFn | 6731.2640 | 1477.132 | 4.557 | 0.000 | 3829.450 | 9633.079 |
| GarageFinish_Unf | 5742.0994 | 1736.538 | 3.307 | 0.001 | 2330.683 | 9153.516 |
| GarageQual_Ex | 7127.8693 | 6099.030 | 1.169 | 0.243 | -4853.632 | 1.91e+04 |
| GarageQual_Fa | 2101.8684 | 5578.248 | 0.377 | 0.706 | -8856.561 | 1.31e+04 |
| GarageQual_Gd | 609.3395 | 7658.405 | 0.080 | 0.937 | -1.44e+04 | 1.57e+04 |
| GarageQual_Po | 0 | 0 | nan | nan | 0 | 0 |
| GarageQual_TA | 6275.2856 | 4051.434 | 1.549 | 0.122 | -1683.728 | 1.42e+04 |
| GarageCond_Ex | 7127.8693 | 6099.030 | 1.169 | 0.243 | -4853.632 | 1.91e+04 |
| GarageCond_Fa | -4718.4580 | 8188.980 | -0.576 | 0.565 | -2.08e+04 | 1.14e+04 |
| GarageCond_Gd | 1.587e+04 | 1.4e+04 | 1.135 | 0.257 | -1.16e+04 | 4.34e+04 |
| GarageCond_Po | -3215.0442 | 2.16e+04 | -0.149 | 0.882 | -4.56e+04 | 3.92e+04 |
| GarageCond_TA | 1048.3214 | 7475.140 | 0.140 | 0.889 | -1.36e+04 | 1.57e+04 |
| PavedDrive_N | 9346.5553 | 3438.231 | 2.718 | 0.007 | 2592.175 | 1.61e+04 |
| PavedDrive_P | -953.1221 | 4757.017 | -0.200 | 0.841 | -1.03e+04 | 8392.003 |
| PavedDrive_Y | 7720.9297 | 3106.094 | 2.486 | 0.013 | 1619.030 | 1.38e+04 |
| Fence_GdPrv | 5947.7226 | 4219.920 | 1.409 | 0.159 | -2342.279 | 1.42e+04 |
| Fence_GdWo | 9617.4168 | 3709.081 | 2.593 | 0.010 | 2330.954 | 1.69e+04 |
| Fence_MnPrv | 7229.1092 | 2717.115 | 2.661 | 0.008 | 1891.357 | 1.26e+04 |
| Fence_MnWw | -6679.8858 | 6636.636 | -1.007 | 0.315 | -1.97e+04 | 6357.738 |
| SaleType_COD | -1618.4116 | 6526.789 | -0.248 | 0.804 | -1.44e+04 | 1.12e+04 |
| SaleType_CWD | 5.253e+04 | 1.26e+04 | 4.168 | 0.000 | 2.78e+04 | 7.73e+04 |
| SaleType_Con | 5743.7135 | 1.63e+04 | 0.353 | 0.724 | -2.62e+04 | 3.77e+04 |
| SaleType_ConLD | -7598.1329 | 1.07e+04 | -0.711 | 0.477 | -2.86e+04 | 1.34e+04 |
| SaleType_ConLI | -4069.6281 | 1.81e+04 | -0.225 | 0.822 | -3.96e+04 | 3.15e+04 |
| SaleType_ConLw | -9888.1004 | 1.32e+04 | -0.748 | 0.455 | -3.59e+04 | 1.61e+04 |
| SaleType_New | -2.665e+04 | 1.3e+04 | -2.049 | 0.041 | -5.22e+04 | -1102.959 |
| SaleType_Oth | 6907.1675 | 1.21e+04 | 0.571 | 0.568 | -1.69e+04 | 3.07e+04 |
| SaleType_WD | 763.1038 | 4724.799 | 0.162 | 0.872 | -8518.729 | 1e+04 |
| SaleCondition_Abnorml | -5180.2760 | 5587.306 | -0.927 | 0.354 | -1.62e+04 | 5795.946 |
| SaleCondition_AdjLand | 2.61e+04 | 1.79e+04 | 1.460 | 0.145 | -9017.280 | 6.12e+04 |
| SaleCondition_Alloca | -4.918e+04 | 1.71e+04 | -2.877 | 0.004 | -8.28e+04 | -1.56e+04 |
| SaleCondition_Family | 8611.0939 | 7932.263 | 1.086 | 0.278 | -6971.780 | 2.42e+04 |
| SaleCondition_Normal | 107.0441 | 5400.131 | 0.020 | 0.984 | -1.05e+04 | 1.07e+04 |
| SaleCondition_Partial | 3.566e+04 | 1.3e+04 | 2.734 | 0.006 | 1e+04 | 6.13e+04 |
| Omnibus: | 136.128 | Durbin-Watson: | 1.911 |
|---|---|---|---|
| Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 2761.370 |
| Skew: | 0.089 | Prob(JB): | 0.00 |
| Kurtosis: | 12.553 | Cond. No. | 1.25e+16 |
rsq=round(model.rsquared_adj,4)
rsq
0.9346
model.pvalues.sort_values()
Neighborhood_StoneBr 5.352529e-18
KitchenAbvGr 3.439900e-11
Utilities_AllPub 3.439900e-11
BsmtHalfBath 3.439900e-11
BsmtExposure_Gd 1.533103e-08
...
HeatingQC_Po NaN
Electrical_Mix NaN
Functional_Sev NaN
GarageType_2Types NaN
GarageQual_Po NaN
Length: 276, dtype: float64
col_to_drop=model.pvalues.sort_values().index[-1]
Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)
print('column to drop',col_to_drop)
Adjusted r_squared 0.9406 column to drop Exterior2nd_CBlock
Xnew.columns
Index(['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',
'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', '2ndFlrSF', 'GrLivArea',
...
'GarageQual_TA', 'GarageCond_Fa', 'GarageCond_Gd', 'Fence_GdPrv',
'Fence_GdWo', 'Fence_MnPrv', 'SaleType_CWD', 'SaleCondition_Abnorml',
'SaleCondition_Alloca', 'SaleCondition_Partial'],
dtype='object', length=113)
Xnew.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1038 entries, 0 to 1037 Columns: 113 entries, MSSubClass to SaleCondition_Partial dtypes: float64(18), uint8(95) memory usage: 242.4 KB
Xnew=Xnew.drop(labels=['Exterior1st_ImStucc','HouseStyle_2.5Fin'],axis=1)
Xnew.columns
Index(['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',
'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', '2ndFlrSF', 'GrLivArea',
...
'GarageQual_TA', 'GarageCond_Fa', 'GarageCond_Gd', 'Fence_GdPrv',
'Fence_GdWo', 'Fence_MnPrv', 'SaleType_CWD', 'SaleCondition_Abnorml',
'SaleCondition_Alloca', 'SaleCondition_Partial'],
dtype='object', length=111)
from sklearn.linear_model import LinearRegression
lm=LinearRegression()
model=lm.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)
from sklearn.metrics import mean_squared_error,mean_absolute_error
tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)
print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)
train_err 228448012.2987952 test_err 4.273445948639171e+26 train_ab 10649.578313253012 test_ab 1433367142066.1394
len('Xtrain')
6
from sklearn.linear_model import Ridge
rr=Ridge(alpha=23)
model=rr.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)
tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)
print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)
train_err 306758235.53366125 test_err 453827951.64898556 train_ab 12176.956117645803 test_ab 14044.049405250164
w=[]
e=0.01
for i in range(0,1000,1):
w.append(e)
e=round(e+0.01,2)
w
[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1, 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.2, 1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3, 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.38, 1.39, 1.4, 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6, 1.61, 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69, 1.7, 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79, 1.8, 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88, 1.89, 1.9, 1.91, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97, 1.98, 1.99, 2.0, 2.01, 2.02, 2.03, 2.04, 2.05, 2.06, 2.07, 2.08, 2.09, 2.1, 2.11, 2.12, 2.13, 2.14, 2.15, 2.16, 2.17, 2.18, 2.19, 2.2, 2.21, 2.22, 2.23, 2.24, 2.25, 2.26, 2.27, 2.28, 2.29, 2.3, 2.31, 2.32, 2.33, 2.34, 2.35, 2.36, 2.37, 2.38, 2.39, 2.4, 2.41, 2.42, 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5, 2.51, 2.52, 2.53, 2.54, 2.55, 2.56, 2.57, 2.58, 2.59, 2.6, 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69, 2.7, 2.71, 2.72, 2.73, 2.74, 2.75, 2.76, 2.77, 2.78, 2.79, 2.8, 2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87, 2.88, 2.89, 2.9, 2.91, 2.92, 2.93, 2.94, 2.95, 2.96, 2.97, 2.98, 2.99, 3.0, 3.01, 3.02, 3.03, 3.04, 3.05, 3.06, 3.07, 3.08, 3.09, 3.1, 3.11, 3.12, 3.13, 3.14, 3.15, 3.16, 3.17, 3.18, 3.19, 3.2, 3.21, 3.22, 3.23, 3.24, 3.25, 3.26, 3.27, 3.28, 3.29, 3.3, 3.31, 3.32, 3.33, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.4, 3.41, 3.42, 3.43, 3.44, 3.45, 3.46, 3.47, 3.48, 3.49, 3.5, 3.51, 3.52, 3.53, 3.54, 3.55, 3.56, 3.57, 3.58, 3.59, 3.6, 3.61, 3.62, 3.63, 3.64, 3.65, 3.66, 3.67, 3.68, 3.69, 3.7, 3.71, 3.72, 3.73, 3.74, 3.75, 3.76, 3.77, 3.78, 3.79, 3.8, 3.81, 3.82, 3.83, 3.84, 3.85, 3.86, 3.87, 3.88, 3.89, 3.9, 3.91, 3.92, 3.93, 3.94, 3.95, 3.96, 3.97, 3.98, 3.99, 4.0, 4.01, 4.02, 4.03, 4.04, 4.05, 4.06, 4.07, 4.08, 4.09, 4.1, 4.11, 4.12, 4.13, 4.14, 4.15, 4.16, 4.17, 4.18, 4.19, 4.2, 4.21, 4.22, 4.23, 4.24, 4.25, 4.26, 4.27, 4.28, 4.29, 4.3, 4.31, 4.32, 4.33, 4.34, 4.35, 4.36, 4.37, 4.38, 4.39, 4.4, 4.41, 4.42, 4.43, 4.44, 4.45, 4.46, 4.47, 4.48, 4.49, 4.5, 4.51, 4.52, 4.53, 4.54, 4.55, 4.56, 4.57, 4.58, 4.59, 4.6, 4.61, 4.62, 4.63, 4.64, 4.65, 4.66, 4.67, 4.68, 4.69, 4.7, 4.71, 4.72, 4.73, 4.74, 4.75, 4.76, 4.77, 4.78, 4.79, 4.8, 4.81, 4.82, 4.83, 4.84, 4.85, 4.86, 4.87, 4.88, 4.89, 4.9, 4.91, 4.92, 4.93, 4.94, 4.95, 4.96, 4.97, 4.98, 4.99, 5.0, 5.01, 5.02, 5.03, 5.04, 5.05, 5.06, 5.07, 5.08, 5.09, 5.1, 5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.2, 5.21, 5.22, 5.23, 5.24, 5.25, 5.26, 5.27, 5.28, 5.29, 5.3, 5.31, 5.32, 5.33, 5.34, 5.35, 5.36, 5.37, 5.38, 5.39, 5.4, 5.41, 5.42, 5.43, 5.44, 5.45, 5.46, 5.47, 5.48, 5.49, 5.5, 5.51, 5.52, 5.53, 5.54, 5.55, 5.56, 5.57, 5.58, 5.59, 5.6, 5.61, 5.62, 5.63, 5.64, 5.65, 5.66, 5.67, 5.68, 5.69, 5.7, 5.71, 5.72, 5.73, 5.74, 5.75, 5.76, 5.77, 5.78, 5.79, 5.8, 5.81, 5.82, 5.83, 5.84, 5.85, 5.86, 5.87, 5.88, 5.89, 5.9, 5.91, 5.92, 5.93, 5.94, 5.95, 5.96, 5.97, 5.98, 5.99, 6.0, 6.01, 6.02, 6.03, 6.04, 6.05, 6.06, 6.07, 6.08, 6.09, 6.1, 6.11, 6.12, 6.13, 6.14, 6.15, 6.16, 6.17, 6.18, 6.19, 6.2, 6.21, 6.22, 6.23, 6.24, 6.25, 6.26, 6.27, 6.28, 6.29, 6.3, 6.31, 6.32, 6.33, 6.34, 6.35, 6.36, 6.37, 6.38, 6.39, 6.4, 6.41, 6.42, 6.43, 6.44, 6.45, 6.46, 6.47, 6.48, 6.49, 6.5, 6.51, 6.52, 6.53, 6.54, 6.55, 6.56, 6.57, 6.58, 6.59, 6.6, 6.61, 6.62, 6.63, 6.64, 6.65, 6.66, 6.67, 6.68, 6.69, 6.7, 6.71, 6.72, 6.73, 6.74, 6.75, 6.76, 6.77, 6.78, 6.79, 6.8, 6.81, 6.82, 6.83, 6.84, 6.85, 6.86, 6.87, 6.88, 6.89, 6.9, 6.91, 6.92, 6.93, 6.94, 6.95, 6.96, 6.97, 6.98, 6.99, 7.0, 7.01, 7.02, 7.03, 7.04, 7.05, 7.06, 7.07, 7.08, 7.09, 7.1, 7.11, 7.12, 7.13, 7.14, 7.15, 7.16, 7.17, 7.18, 7.19, 7.2, 7.21, 7.22, 7.23, 7.24, 7.25, 7.26, 7.27, 7.28, 7.29, 7.3, 7.31, 7.32, 7.33, 7.34, 7.35, 7.36, 7.37, 7.38, 7.39, 7.4, 7.41, 7.42, 7.43, 7.44, 7.45, 7.46, 7.47, 7.48, 7.49, 7.5, 7.51, 7.52, 7.53, 7.54, 7.55, 7.56, 7.57, 7.58, 7.59, 7.6, 7.61, 7.62, 7.63, 7.64, 7.65, 7.66, 7.67, 7.68, 7.69, 7.7, 7.71, 7.72, 7.73, 7.74, 7.75, 7.76, 7.77, 7.78, 7.79, 7.8, 7.81, 7.82, 7.83, 7.84, 7.85, 7.86, 7.87, 7.88, 7.89, 7.9, 7.91, 7.92, 7.93, 7.94, 7.95, 7.96, 7.97, 7.98, 7.99, 8.0, 8.01, 8.02, 8.03, 8.04, 8.05, 8.06, 8.07, 8.08, 8.09, 8.1, 8.11, 8.12, 8.13, 8.14, 8.15, 8.16, 8.17, 8.18, 8.19, 8.2, 8.21, 8.22, 8.23, 8.24, 8.25, 8.26, 8.27, 8.28, 8.29, 8.3, 8.31, 8.32, 8.33, 8.34, 8.35, 8.36, 8.37, 8.38, 8.39, 8.4, 8.41, 8.42, 8.43, 8.44, 8.45, 8.46, 8.47, 8.48, 8.49, 8.5, 8.51, 8.52, 8.53, 8.54, 8.55, 8.56, 8.57, 8.58, 8.59, 8.6, 8.61, 8.62, 8.63, 8.64, 8.65, 8.66, 8.67, 8.68, 8.69, 8.7, 8.71, 8.72, 8.73, 8.74, 8.75, 8.76, 8.77, 8.78, 8.79, 8.8, 8.81, 8.82, 8.83, 8.84, 8.85, 8.86, 8.87, 8.88, 8.89, 8.9, 8.91, 8.92, 8.93, 8.94, 8.95, 8.96, 8.97, 8.98, 8.99, 9.0, 9.01, 9.02, 9.03, 9.04, 9.05, 9.06, 9.07, 9.08, 9.09, 9.1, 9.11, 9.12, 9.13, 9.14, 9.15, 9.16, 9.17, 9.18, 9.19, 9.2, 9.21, 9.22, 9.23, 9.24, 9.25, 9.26, 9.27, 9.28, 9.29, 9.3, 9.31, 9.32, 9.33, 9.34, 9.35, 9.36, 9.37, 9.38, 9.39, 9.4, 9.41, 9.42, 9.43, 9.44, 9.45, 9.46, 9.47, 9.48, 9.49, 9.5, 9.51, 9.52, 9.53, 9.54, 9.55, 9.56, 9.57, 9.58, 9.59, 9.6, 9.61, 9.62, 9.63, 9.64, 9.65, 9.66, 9.67, 9.68, 9.69, 9.7, 9.71, 9.72, 9.73, 9.74, 9.75, 9.76, 9.77, 9.78, 9.79, 9.8, 9.81, 9.82, 9.83, 9.84, 9.85, 9.86, 9.87, 9.88, 9.89, 9.9, 9.91, 9.92, 9.93, 9.94, 9.95, 9.96, 9.97, 9.98, 9.99, 10.0]
rr=Ridge()
tg={'alpha':w}
from sklearn.model_selection import GridSearchCV
cv=GridSearchCV(rr,tg,scoring='neg_mean_absolute_error',cv=4)
cvmodel=cv.fit(Xnew,Y)
cvmodel.best_params_
{'alpha': 3.09}
rr=Ridge(alpha=3.09)
model=rr.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)
tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)
print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)
train_err 244994310.80139524 test_err 443235239.3342266 train_ab 10911.75380887483 test_ab 13737.790741817766
cvmodel.best_estimator_
Ridge(alpha=3.09)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Ridge(alpha=3.09)
df2=pd.read_csv('Downloads/testing_set.csv')
df2
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | ... | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1461 | 20 | RH | 80.0 | 11622 | Pave | NaN | Reg | Lvl | AllPub | ... | 120 | 0 | NaN | MnPrv | NaN | 0 | 6 | 2010 | WD | Normal |
| 1 | 1462 | 20 | RL | 81.0 | 14267 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | 0 | NaN | NaN | Gar2 | 12500 | 6 | 2010 | WD | Normal |
| 2 | 1463 | 60 | RL | 74.0 | 13830 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | 0 | NaN | MnPrv | NaN | 0 | 3 | 2010 | WD | Normal |
| 3 | 1464 | 60 | RL | 78.0 | 9978 | Pave | NaN | IR1 | Lvl | AllPub | ... | 0 | 0 | NaN | NaN | NaN | 0 | 6 | 2010 | WD | Normal |
| 4 | 1465 | 120 | RL | 43.0 | 5005 | Pave | NaN | IR1 | HLS | AllPub | ... | 144 | 0 | NaN | NaN | NaN | 0 | 1 | 2010 | WD | Normal |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1454 | 2915 | 160 | RM | 21.0 | 1936 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | 0 | NaN | NaN | NaN | 0 | 6 | 2006 | WD | Normal |
| 1455 | 2916 | 160 | RM | 21.0 | 1894 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | 0 | NaN | NaN | NaN | 0 | 4 | 2006 | WD | Abnorml |
| 1456 | 2917 | 20 | RL | 160.0 | 20000 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2006 | WD | Abnorml |
| 1457 | 2918 | 85 | RL | 62.0 | 10441 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | 0 | NaN | MnPrv | Shed | 700 | 7 | 2006 | WD | Normal |
| 1458 | 2919 | 60 | RL | 74.0 | 9627 | Pave | NaN | Reg | Lvl | AllPub | ... | 0 | 0 | NaN | NaN | NaN | 0 | 11 | 2006 | WD | Normal |
1459 rows × 80 columns
df2.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1459 entries, 0 to 1458 Data columns (total 80 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1459 non-null int64 1 MSSubClass 1459 non-null int64 2 MSZoning 1455 non-null object 3 LotFrontage 1232 non-null float64 4 LotArea 1459 non-null int64 5 Street 1459 non-null object 6 Alley 107 non-null object 7 LotShape 1459 non-null object 8 LandContour 1459 non-null object 9 Utilities 1457 non-null object 10 LotConfig 1459 non-null object 11 LandSlope 1459 non-null object 12 Neighborhood 1459 non-null object 13 Condition1 1459 non-null object 14 Condition2 1459 non-null object 15 BldgType 1459 non-null object 16 HouseStyle 1459 non-null object 17 OverallQual 1459 non-null int64 18 OverallCond 1459 non-null int64 19 YearBuilt 1459 non-null int64 20 YearRemodAdd 1459 non-null int64 21 RoofStyle 1459 non-null object 22 RoofMatl 1459 non-null object 23 Exterior1st 1458 non-null object 24 Exterior2nd 1458 non-null object 25 MasVnrType 1443 non-null object 26 MasVnrArea 1444 non-null float64 27 ExterQual 1459 non-null object 28 ExterCond 1459 non-null object 29 Foundation 1459 non-null object 30 BsmtQual 1415 non-null object 31 BsmtCond 1414 non-null object 32 BsmtExposure 1415 non-null object 33 BsmtFinType1 1417 non-null object 34 BsmtFinSF1 1458 non-null float64 35 BsmtFinType2 1417 non-null object 36 BsmtFinSF2 1458 non-null float64 37 BsmtUnfSF 1458 non-null float64 38 TotalBsmtSF 1458 non-null float64 39 Heating 1459 non-null object 40 HeatingQC 1459 non-null object 41 CentralAir 1459 non-null object 42 Electrical 1459 non-null object 43 1stFlrSF 1459 non-null int64 44 2ndFlrSF 1459 non-null int64 45 LowQualFinSF 1459 non-null int64 46 GrLivArea 1459 non-null int64 47 BsmtFullBath 1457 non-null float64 48 BsmtHalfBath 1457 non-null float64 49 FullBath 1459 non-null int64 50 HalfBath 1459 non-null int64 51 BedroomAbvGr 1459 non-null int64 52 KitchenAbvGr 1459 non-null int64 53 KitchenQual 1458 non-null object 54 TotRmsAbvGrd 1459 non-null int64 55 Functional 1457 non-null object 56 Fireplaces 1459 non-null int64 57 FireplaceQu 729 non-null object 58 GarageType 1383 non-null object 59 GarageYrBlt 1381 non-null float64 60 GarageFinish 1381 non-null object 61 GarageCars 1458 non-null float64 62 GarageArea 1458 non-null float64 63 GarageQual 1381 non-null object 64 GarageCond 1381 non-null object 65 PavedDrive 1459 non-null object 66 WoodDeckSF 1459 non-null int64 67 OpenPorchSF 1459 non-null int64 68 EnclosedPorch 1459 non-null int64 69 3SsnPorch 1459 non-null int64 70 ScreenPorch 1459 non-null int64 71 PoolArea 1459 non-null int64 72 PoolQC 3 non-null object 73 Fence 290 non-null object 74 MiscFeature 51 non-null object 75 MiscVal 1459 non-null int64 76 MoSold 1459 non-null int64 77 YrSold 1459 non-null int64 78 SaleType 1458 non-null object 79 SaleCondition 1459 non-null object dtypes: float64(11), int64(26), object(43) memory usage: 912.0+ KB
df2.shape
(1459, 80)
df2.isna().sum()
Id 0
MSSubClass 0
MSZoning 4
LotFrontage 227
LotArea 0
...
MiscVal 0
MoSold 0
YrSold 0
SaleType 1
SaleCondition 0
Length: 80, dtype: int64
for i in df2.columns:
if (df2[i].isna().sum())>0:
if df2[i].dtypes=='object':
x=df2[i].mode()[0]
df2[i]=df2[i].fillna(x)
else:
x=df2[i].mean()
df2[i]=df2[i].fillna(x)
df2.isna().sum()
Id 0
MSSubClass 0
MSZoning 0
LotFrontage 0
LotArea 0
..
MiscVal 0
MoSold 0
YrSold 0
SaleType 0
SaleCondition 0
Length: 80, dtype: int64
Xts=df2.drop(labels=['Id','LowQualFinSF','MiscVal'],axis=1)
Xts.shape
(1459, 77)
cat_ts=[]
con_ts=[]
for i in Xts.columns:
if Xts[i].dtypes=='object':
cat_ts.append(i)
else:
con_ts.append(i)
print(cat_ts)
print(con_ts)
['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'] ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold']
cat_ts=['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood',
'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd',
'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',
'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu',
'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType',
'SaleCondition']
con_ts=['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea',
'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath',
'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt',
'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',
'MoSold', 'YrSold']
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
X1ts=pd.DataFrame(ss.fit_transform(Xts[con_ts]),columns=con_ts)
X1ts
| MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | ... | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MoSold | YrSold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.874711 | 0.555587 | 0.363929 | -0.751101 | 0.400766 | -0.340945 | -1.072885 | -0.570108 | 0.063295 | 0.517348 | ... | -0.988013 | 1.185945 | 0.366678 | -0.701628 | -0.360738 | -0.088827 | 1.818960 | -0.057227 | -0.038281 | 1.713905 |
| 1 | -0.874711 | 0.604239 | 0.897861 | -0.054877 | 0.400766 | -0.439695 | -1.214908 | 0.041273 | 1.063392 | -0.297903 | ... | -0.988013 | -0.741213 | 2.347867 | -0.178826 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | -0.038281 | 1.713905 |
| 2 | 0.061351 | 0.263676 | 0.809646 | -0.751101 | -0.497418 | 0.844059 | 0.678742 | -0.570108 | 0.773254 | -0.297903 | ... | 0.301623 | 0.042559 | 0.930495 | -0.207871 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | -1.140614 | 1.713905 |
| 3 | 0.061351 | 0.458284 | 0.032064 | -0.054877 | 0.400766 | 0.876976 | 0.678742 | -0.456889 | 0.357829 | -0.297903 | ... | 0.301623 | -0.012766 | 2.089451 | -0.178826 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | -0.038281 | 1.713905 |
| 4 | 1.465443 | -1.244533 | -0.971808 | 1.337571 | -0.497418 | 0.679475 | 0.394694 | -0.570108 | -0.387298 | -0.297903 | ... | 0.301623 | 0.153210 | -0.729632 | 0.489198 | -0.360738 | -0.088827 | 2.243060 | -0.057227 | -1.875504 | 1.713905 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1454 | 2.401505 | -2.314875 | -1.591330 | -1.447325 | 1.298950 | -0.044694 | -0.646813 | -0.570108 | -0.965376 | -0.297903 | ... | -2.277648 | -2.179665 | -0.729632 | -0.701628 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | -0.038281 | -1.359958 |
| 1455 | 2.401505 | -2.314875 | -1.599808 | -1.447325 | -0.497418 | -0.044694 | -0.646813 | -0.570108 | -0.411477 | -0.297903 | ... | -0.988013 | -0.861084 | -0.729632 | -0.353093 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | -0.773170 | -1.359958 |
| 1456 | -0.874711 | 4.447740 | 2.055150 | -0.751101 | 1.298950 | -0.373861 | 0.584059 | -0.570108 | 1.724994 | -0.297903 | ... | 0.301623 | 0.475939 | 2.982161 | -0.701628 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | 1.064053 | -1.359958 |
| 1457 | 0.646389 | -0.320147 | 0.125527 | -0.751101 | -0.497418 | 0.679475 | 0.394694 | -0.570108 | -0.224645 | -0.297903 | ... | -2.277648 | -2.179665 | -0.103169 | -0.236915 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | 0.329164 | -1.359958 |
| 1458 | 0.061351 | 0.263676 | -0.038790 | 0.641347 | -0.497418 | 0.712392 | 0.489377 | -0.037980 | 0.700719 | -0.297903 | ... | 1.591258 | 0.817111 | 0.758218 | -0.004559 | -0.360738 | -0.088827 | -0.301543 | -0.057227 | 1.798942 | -1.359958 |
1459 rows × 34 columns
X2ts=pd.get_dummies(Xts[cat_ts])
X2ts
| MSZoning_C (all) | MSZoning_FV | MSZoning_RH | MSZoning_RL | MSZoning_RM | Street_Grvl | Street_Pave | Alley_Grvl | Alley_Pave | LotShape_IR1 | ... | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1454 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1455 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 1456 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 1457 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1458 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
1459 rows × 234 columns
Xnew_ts=X1ts.join(X2ts)
Xnew_ts.shape
(1459, 268)
Xnew_ts.index=range(0,1459)
keep=[]
drop=[]
for i in Xnew_ts.columns:
if i in Xnew.columns:
keep.append(i)
else:
drop.append(i)
keep
['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', '2ndFlrSF', 'GrLivArea', 'BsmtHalfBath', 'FullBath', 'KitchenAbvGr', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'MSZoning_C (all)', 'MSZoning_FV', 'LandContour_Bnk', 'LandContour_HLS', 'LandContour_Low', 'LandContour_Lvl', 'Utilities_AllPub', 'LotConfig_Corner', 'LotConfig_CulDSac', 'LotConfig_Inside', 'Neighborhood_Blmngtn', 'Neighborhood_CollgCr', 'Neighborhood_Crawfor', 'Neighborhood_Edwards', 'Neighborhood_Gilbert', 'Neighborhood_MeadowV', 'Neighborhood_Mitchel', 'Neighborhood_NAmes', 'Neighborhood_NPkVill', 'Neighborhood_NWAmes', 'Neighborhood_NoRidge', 'Neighborhood_NridgHt', 'Neighborhood_OldTown', 'Neighborhood_Sawyer', 'Neighborhood_SawyerW', 'Neighborhood_StoneBr', 'Neighborhood_Timber', 'Condition1_Artery', 'Condition1_RRAe', 'Condition1_RRAn', 'BldgType_Duplex', 'HouseStyle_2Story', 'HouseStyle_SFoyer', 'HouseStyle_SLvl', 'RoofStyle_Hip', 'RoofStyle_Mansard', 'RoofMatl_CompShg', 'Exterior1st_AsbShng', 'Exterior1st_BrkFace', 'Exterior1st_CBlock', 'Exterior1st_CemntBd', 'Exterior1st_HdBoard', 'Exterior1st_Wd Sdng', 'Exterior2nd_BrkFace', 'Exterior2nd_CBlock', 'Exterior2nd_CmentBd', 'Exterior2nd_HdBoard', 'Exterior2nd_ImStucc', 'Exterior2nd_MetalSd', 'Exterior2nd_Stucco', 'Exterior2nd_VinylSd', 'Exterior2nd_Wd Sdng', 'MasVnrType_Stone', 'ExterQual_Ex', 'ExterCond_TA', 'BsmtQual_Ex', 'BsmtQual_Fa', 'BsmtQual_Gd', 'BsmtCond_Gd', 'BsmtCond_TA', 'BsmtExposure_Av', 'BsmtExposure_Gd', 'BsmtFinType1_ALQ', 'BsmtFinType1_GLQ', 'BsmtFinType1_Unf', 'BsmtFinType2_ALQ', 'BsmtFinType2_GLQ', 'BsmtFinType2_Unf', 'Heating_Wall', 'CentralAir_N', 'CentralAir_Y', 'KitchenQual_Ex', 'Functional_Min1', 'Functional_Min2', 'Functional_Typ', 'FireplaceQu_Gd', 'FireplaceQu_Po', 'FireplaceQu_TA', 'GarageType_Attchd', 'GarageType_CarPort', 'GarageFinish_Fin', 'GarageFinish_RFn', 'GarageFinish_Unf', 'GarageQual_TA', 'GarageCond_Fa', 'GarageCond_Gd', 'Fence_GdPrv', 'Fence_GdWo', 'Fence_MnPrv', 'SaleType_CWD', 'SaleCondition_Abnorml', 'SaleCondition_Alloca', 'SaleCondition_Partial']
drop
['LotFrontage', 'YearRemodAdd', 'MasVnrArea', 'BsmtUnfSF', '1stFlrSF', 'BsmtFullBath', 'HalfBath', 'BedroomAbvGr', 'TotRmsAbvGrd', 'GarageYrBlt', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold', 'MSZoning_RH', 'MSZoning_RL', 'MSZoning_RM', 'Street_Grvl', 'Street_Pave', 'Alley_Grvl', 'Alley_Pave', 'LotShape_IR1', 'LotShape_IR2', 'LotShape_IR3', 'LotShape_Reg', 'LotConfig_FR2', 'LotConfig_FR3', 'LandSlope_Gtl', 'LandSlope_Mod', 'LandSlope_Sev', 'Neighborhood_Blueste', 'Neighborhood_BrDale', 'Neighborhood_BrkSide', 'Neighborhood_ClearCr', 'Neighborhood_IDOTRR', 'Neighborhood_SWISU', 'Neighborhood_Somerst', 'Neighborhood_Veenker', 'Condition1_Feedr', 'Condition1_Norm', 'Condition1_PosA', 'Condition1_PosN', 'Condition1_RRNe', 'Condition1_RRNn', 'Condition2_Artery', 'Condition2_Feedr', 'Condition2_Norm', 'Condition2_PosA', 'Condition2_PosN', 'BldgType_1Fam', 'BldgType_2fmCon', 'BldgType_Twnhs', 'BldgType_TwnhsE', 'HouseStyle_1.5Fin', 'HouseStyle_1.5Unf', 'HouseStyle_1Story', 'HouseStyle_2.5Unf', 'RoofStyle_Flat', 'RoofStyle_Gable', 'RoofStyle_Gambrel', 'RoofStyle_Shed', 'RoofMatl_Tar&Grv', 'RoofMatl_WdShake', 'RoofMatl_WdShngl', 'Exterior1st_AsphShn', 'Exterior1st_BrkComm', 'Exterior1st_MetalSd', 'Exterior1st_Plywood', 'Exterior1st_Stucco', 'Exterior1st_VinylSd', 'Exterior1st_WdShing', 'Exterior2nd_AsbShng', 'Exterior2nd_AsphShn', 'Exterior2nd_Brk Cmn', 'Exterior2nd_Plywood', 'Exterior2nd_Stone', 'Exterior2nd_Wd Shng', 'MasVnrType_BrkCmn', 'MasVnrType_BrkFace', 'MasVnrType_None', 'ExterQual_Fa', 'ExterQual_Gd', 'ExterQual_TA', 'ExterCond_Ex', 'ExterCond_Fa', 'ExterCond_Gd', 'ExterCond_Po', 'Foundation_BrkTil', 'Foundation_CBlock', 'Foundation_PConc', 'Foundation_Slab', 'Foundation_Stone', 'Foundation_Wood', 'BsmtQual_TA', 'BsmtCond_Fa', 'BsmtCond_Po', 'BsmtExposure_Mn', 'BsmtExposure_No', 'BsmtFinType1_BLQ', 'BsmtFinType1_LwQ', 'BsmtFinType1_Rec', 'BsmtFinType2_BLQ', 'BsmtFinType2_LwQ', 'BsmtFinType2_Rec', 'Heating_GasA', 'Heating_GasW', 'Heating_Grav', 'HeatingQC_Ex', 'HeatingQC_Fa', 'HeatingQC_Gd', 'HeatingQC_Po', 'HeatingQC_TA', 'Electrical_FuseA', 'Electrical_FuseF', 'Electrical_FuseP', 'Electrical_SBrkr', 'KitchenQual_Fa', 'KitchenQual_Gd', 'KitchenQual_TA', 'Functional_Maj1', 'Functional_Maj2', 'Functional_Mod', 'Functional_Sev', 'FireplaceQu_Ex', 'FireplaceQu_Fa', 'GarageType_2Types', 'GarageType_Basment', 'GarageType_BuiltIn', 'GarageType_Detchd', 'GarageQual_Fa', 'GarageQual_Gd', 'GarageQual_Po', 'GarageCond_Ex', 'GarageCond_Po', 'GarageCond_TA', 'PavedDrive_N', 'PavedDrive_P', 'PavedDrive_Y', 'PoolQC_Ex', 'PoolQC_Gd', 'Fence_MnWw', 'MiscFeature_Gar2', 'MiscFeature_Othr', 'MiscFeature_Shed', 'SaleType_COD', 'SaleType_Con', 'SaleType_ConLD', 'SaleType_ConLI', 'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD', 'SaleCondition_AdjLand', 'SaleCondition_Family', 'SaleCondition_Normal']
len(keep)
111
len(Xnew_ts.columns)
268
Xnewtest=Xnew_ts[keep]
len(Xnewtest.columns)
111
Xnewtest
| MSSubClass | LotArea | OverallQual | OverallCond | YearBuilt | BsmtFinSF1 | BsmtFinSF2 | TotalBsmtSF | 2ndFlrSF | GrLivArea | ... | GarageQual_TA | GarageCond_Fa | GarageCond_Gd | Fence_GdPrv | Fence_GdWo | Fence_MnPrv | SaleType_CWD | SaleCondition_Abnorml | SaleCondition_Alloca | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.874711 | 0.363929 | -0.751101 | 0.400766 | -0.340945 | 0.063295 | 0.517348 | -0.370808 | -0.775254 | -1.215588 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 1 | -0.874711 | 0.897861 | -0.054877 | 0.400766 | -0.439695 | 1.063392 | -0.297903 | 0.639144 | -0.775254 | -0.323539 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | 0.061351 | 0.809646 | -0.751101 | -0.497418 | 0.844059 | 0.773254 | -0.297903 | -0.266876 | 0.891944 | 0.294508 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 0.061351 | 0.032064 | -0.054877 | 0.400766 | 0.876976 | 0.357829 | -0.297903 | -0.271395 | 0.837243 | 0.243004 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | 1.465443 | -0.971808 | 1.337571 | -0.497418 | 0.679475 | -0.387298 | -0.297903 | 0.528434 | -0.775254 | -0.424487 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1454 | 2.401505 | -1.591330 | -1.447325 | 1.298950 | -0.044694 | -0.965376 | -0.297903 | -1.129968 | 0.523306 | -0.811797 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
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| 1456 | -0.874711 | 2.055150 | -0.751101 | 1.298950 | -0.373861 | 1.724994 | -0.297903 | 0.401907 | -0.775254 | -0.539856 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
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| 1458 | 0.061351 | -0.038790 | 0.641347 | -0.497418 | 0.712392 | 0.700719 | -0.297903 | -0.113237 | 1.612573 | 1.058827 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
1459 rows × 111 columns
pred=cvmodel.predict(Xnewtest)
pred
array([128619.54163418, 181267.41238455, 203420.31328191, ...,
194632.16524631, 113066.61351441, 220273.64798307])
predn={'Output':list(pred)}
predn
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...]}
ID=df2['Id']
ID
0 1461
1 1462
2 1463
3 1464
4 1465
...
1454 2915
1455 2916
1456 2917
1457 2918
1458 2919
Name: Id, Length: 1459, dtype: int64
df_final1=pd.DataFrame(ID)
df_final1
| Id | |
|---|---|
| 0 | 1461 |
| 1 | 1462 |
| 2 | 1463 |
| 3 | 1464 |
| 4 | 1465 |
| ... | ... |
| 1454 | 2915 |
| 1455 | 2916 |
| 1456 | 2917 |
| 1457 | 2918 |
| 1458 | 2919 |
1459 rows × 1 columns
df_final2=pd.DataFrame(predn)
df_final2
| Output | |
|---|---|
| 0 | 128619.541634 |
| 1 | 181267.412385 |
| 2 | 203420.313282 |
| 3 | 208505.126292 |
| 4 | 193287.829533 |
| ... | ... |
| 1454 | 80304.088670 |
| 1455 | 79292.599894 |
| 1456 | 194632.165246 |
| 1457 | 113066.613514 |
| 1458 | 220273.647983 |
1459 rows × 1 columns
df_final=df_final1.join(df_final2)
df_final
| Id | Output | |
|---|---|---|
| 0 | 1461 | 128619.541634 |
| 1 | 1462 | 181267.412385 |
| 2 | 1463 | 203420.313282 |
| 3 | 1464 | 208505.126292 |
| 4 | 1465 | 193287.829533 |
| ... | ... | ... |
| 1454 | 2915 | 80304.088670 |
| 1455 | 2916 | 79292.599894 |
| 1456 | 2917 | 194632.165246 |
| 1457 | 2918 | 113066.613514 |
| 1458 | 2919 | 220273.647983 |
1459 rows × 2 columns